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At present, most graphic design methods of 3D animation scenes only get a small part of 3D animation scenes, and all of them are in a 3D coordinate system, with observers as the core, so it is difficult to express the depth information of 3D animation scenes. This project intends to study a plane design method for 3D animation scenes based on deep learning. CNN (Convolutional Neural Network) is used to build a multi-view 3D animation scene generation network, and 3D geometry and structure of objects are reconstructed through multiple or a group of images. On this basis, the feature extraction method of 3D animation scenes is studied, and the collaborative learning model of multiple networks is established to improve the modeling accuracy of 3D animation scenes. The experimental results show that the network model is superior to the method based on multi-view and 0-1 voxel in detecting retrieval performance, and the accuracy rate can reach 91.725%. The multi-view 3D animation scene generation method in this paper has achieved better results than the current advanced methods, which proves that the multi-view feature fusion network proposed in this paper is a more reasonable method to fuse multi-view image features.

The visual system has a strong information processing ability, and visual attention tracking has various applications in various scenes. This paper mainly focuses on the sports scene of ice and snow far mobilization. It constructs a visual attention system model based on eye tracking. It first establishes an eye tracking system framework using deep learning, and improves the gaze estimation by optimizing the feature extraction network. The visual attention system model was constructed using particle filtering based on motion feature cognition. In the eye-tracking visual attention system model experiments, the Accuracy of the improved eye-tracking system in this paper can be significantly improved to 1.13°, and the error of the visual attention system can be kept within 10°. Furthermore, the four ice and snow sports scene types have an average accuracy of 85.47%, and the constructed model performs well. This study offers a guide for effectively combining eye tracking technology and visual attention.

In the contemporary educational environment, the protection and inheritance of traditional music culture faces challenges. The current diverse needs of music education urgently require innovative teaching models to integrate traditional music culture and enhance students’ aesthetic and cultural literacy. The eco-beauty perspective provides a new educational idea that emphasizes the harmonious coexistence with nature and promotes all-around development of students. Combining traditional music culture and modern education technology effectively can be achieved by combining deep learning technology and new paths in music education. This study explores the music teaching model that integrates traditional music culture with deep learning technology, under the perspective of ecological aesthetic education. The study utilizes a mixed methodology, combines quantitative and qualitative Analysis, and compares the experimental design to verify the effectiveness of the teaching model. In this paper, implementing the music teaching model under the perspective of ecological aesthetic education has improved students’ performance in music perception ability, music emotion expression, music composition and performance, and music knowledge theory. The average increase in music perception ability of the experimental class students is 5.18 points, and the average increase in music composition and performance ability is 11.57 points, showing the impact of this teaching mode on the overall improvement of students’ music ability. In this paper, the music teaching mode combining deep learning under the perspective of ecological aesthetic education can effectively integrate traditional music culture, promote the improvement of students’ music literacy, and provide a new teaching strategy for the inheritance and development of traditional music culture.

This paper analyzes the effectiveness of predictive analysis of brain imaging data based on deep learning algorithms, and improves the prediction accuracy and efficiency of brain imaging data through improved methods. The first step is to measure the local consistency of the brain imaging data using Kendall’s concordance coefficient (KCC), and to analyze the differences between the datasets using the two-sample t-test. Secondly, a batch normalized convolutional neural network (BN-CNN)-based prediction method for brain imaging data has been developed. This method extracts spatial and temporal features in two convolutional layers, followed by a fully connected layer for classification. Experimental results show that this method is helpful in predicting missing structural data in brain imaging. Secondly, a batch normalized convolutional neural network (BN-CNN) based brain imaging data prediction method is developed, which extracts spatial and temporal features in two convolutional layers. Then it connects to a fully connected layer for classification. The experimental results show that this method’s structural similarity index (SSIM) and feature similarity index (FSIM) in brain imaging data prediction of missing data reaches 0.9446 and 0.9465, respectively, which is significantly better than that of other GAN benchmarks. In applying the method to epilepsy and Parkinson’s cases, this algorithm is used to epilepsy and Parkinson’s cases, and a two-sample t-test analyzes the differences in the data sets. In the application of epilepsy and Parkinson’s cases, the algorithm in this paper has an average prediction accuracy of 93.37%, effectively reducing the rate of incorrect predictions. Deep learning algorithms are highly efficient and accurate in predicting brain imaging data, which is crucial for future clinical diagnosis and treatment.

In the context of education informatization, this paper discusses how to deeply integrate advanced educational technology with traditional teaching. By constructing a blended teaching model with the goal of deep learning and using linear regression, decision tree and other algorithms to build a data analysis framework, this paper aims to assess the practical application effect of educational technology in universities. The empirical study explores the development of deep blending between educational technology and teaching by taking the Information Technology Course of University J as an example. The results showed that after the teaching, the posttest mean of profound learning ability of students in the mixed, online, and offline groups were 1.948, 0.182 and 0.065 higher than the pretest mean, respectively. z-values ranked in the top three were the perseverance of students in the mixed group (-12.642) and the ability to think creatively (-11.682) and critically (-9.875), which were higher than those of students in the online group and the offline group, the overall profound learning ability of students in the mixed group was effectively improved, which verified the effectiveness of the blended teaching model proposed in this paper.

Currently, in the production of cigarettes in the blade, charging export moisture control means is relatively single and can not effectively guarantee the excellent quality of cigarette filament. In this paper, first of all, the working principle of the tobacco blade charging machine is introduced, and the moisture of the tobacco leaf for the charging machine is dynamically analyzed, and the influence of the return air temperature control of the charging machine on the export moisture of the blade charging process is explored. Secondly, based on the traditional PID controller, an adaptive fuzzy PID controller is established by combining adaptive fuzzy rules, and then the stacked noise-reducing self-encoder in deep learning is combined with the adaptive fuzzy PID control to design the intelligent control structure of export moisture of leaf charging process. Finally, the effectiveness of export moisture intelligence control, process capability index, and the effect before and after application were analyzed in controlled experiments, respectively. The results show that the difference between the predicted value and the real value of blade export moisture in this paper’s method is only 0.5%, and the process capability index of this paper’s method is improved by 1.48 compared with the PID controller, and it can control the temperature of the return air of the charging machine in the range of 56.86℃~57.21℃. The intelligent control method of export moisture introduced by deep learning can accurately control the export moisture of the leaf dosing process, which effectively ensures the quality of tobacco filament making.

In this paper, we use Caffe framework to implement the improved Faster R-CNN recognition technique for building images in civil engineering under Linux system and add feature pyramid network and regional feature aggregation into the ResNet-50 network and ResNet-101 network, respectively, to strengthen the training effect, and establish ResNet-101+FPN+ROI Align image recognition technique. Simulated crack experiments and concrete surface quality defect detection experiments confirm that the ResNet-101 FPN ROI Align method is accurate and detects defects at a high rate. The method established in this paper has a minimum error of only 0.4% in the simulated crack experiment, and the detection rate is much higher than that of other detection methods when detecting quality defects on the concrete surface, and the accuracy can reach up to 94% at the same time. In civil engineering, the image recognition technology established in this paper has practical significance and high application value, as demonstrated by the experiment.

In this paper, a new intelligent robot path recognition system LADDPG, is constructed by extending the DDPG algorithm with the support of the deep learning network algorithm. The path recognition system is optimized by using the memory system and the decision module guided by the attention mechanism, which solves the problems of the existing DRL intelligent robot path recognition method that cannot store long-time memory and the training time is too long. The system in this paper is validated by conducting path recognition experiments under different road conditions and conducting dynamic obstacle avoidance test experiments. The system can maintain excellent noise immunity and a short path recognition time of 51 ms under the conditions of missing road conditions, circuitous road conditions and large curvature, and in the presence of multiple dynamic obstacles, the system can maintain a collision rate close to 0 while maintaining a very high success rate of 0.98, and the required path recognition time is much lower than that of other methods, with an average reward value of 0.4151. This paper’s system is highly accurate in recognizing intelligent robot paths and has high application value and capability.

In this paper, the dependency relationship between different knowledge points is constructed by statistically counting the correct rate of answering knowledge points at adjacent times, finding the prior knowledge of each knowledge point, and the prior of the prior. The dependencies of each knowledge point are stored as key-value pairs and added into the loss function of the model as constraints, thus completing the construction of the deep learning knowledge tracking model. Aiming at the shortcomings of the deep learning knowledge tracking model, we propose to improve the DT-BKT learning knowledge tracking model and use it to design the teaching system of the university English digital course and analyze the teaching system of the university English digital course by using simulation experiments. The results show that the shortest time for acquiring the digital materials of college English is 12.35s, which is 52.87s shorter than that of the conventional English teaching resources information management system, indicating that the system in this paper is more suitable for the development and innovation of college English teaching in colleges and universities. This study can mobilize students’ willingness to participate in English learning in all aspects and has a promoting effect on the development of university English digital course teaching in colleges and universities.

This paper analyzes the current situation of physical education teaching in colleges and universities from the perspective of deep learning and innovates the teaching methodology. A multidimensional data array is used to quantify students’ interests and hobbies to create a recommendation module for intelligent sports learning materials. Using the SIFT feature extraction algorithm, identify the extreme value point of the detection target and develop the sports target extraction module. Through distance transformation and morphological features, determine the skeleton image features to construct a college sports teaching model using deep learning. The results show that the model in this paper enables the coverage rate of sports courses to reach over 97.20%, with a fluctuation of no more than 1.5%. Deep learning has contributed to some innovation in college sports teaching.