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With the promotion and development of artificial intelligence, the computer major has become a hot major in innovation and entrepreneurship education. It is increasingly essential to carry out a refined evaluation of creativity and venture education to achieve professional differentiation. The purpose of this paper is to study and explore the innovative use of AI in the field of innovation and entrepreneurship of college students in Internet universities. In the article, AI is first introduced. AI is inherently a novel instrument of human praxis, a tool of productivity that embodies human ability and volition. Then we analyze the innovation and venture education on the ground of the BP neural network, and finally, we conduct an empirical study on the innovation and venture on the ground of the BP neural network. Innovation and entrepreneurship refer to entrepreneurial activities based on one or several innovations in technological innovation, product innovation, brand innovation, service innovation, business model innovation, management innovation, organizational innovation, market innovation, and channel innovation. The report analyzes the influencing factors of creative venture education and designs an evaluation model of creative venture education. The test shows that the output of the BP neural network is the actual value, the real worth is reasonable, and the desired worth is also reasonable. The maximum error between the two is 1.64%. The results tend to be generally satisfactory. The BP neural network model can highly generalize students' creative venture abilities, increasing the practical application value and research scope of BP neural networks., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Liping Bai et al.)

Entrepreneurship education activities in colleges and universities play an important role in improving students' innovation ability. Therefore, this paper has important practical value to evaluate the innovation and entrepreneurship ability of college students. At present, most studies use qualitative research methods, which is inefficient. Even if quantitative analysis is adopted, it is mostly linear analysis, which is inconsistent with the actual situation. In order to improve the application level of genetic algorithm to the innovation and entrepreneurship ability of universities based on BP neural network, this paper studies the evaluation model of innovation and entrepreneurship ability of universities. Based on the simple analysis of the current situation of university innovation and entrepreneurship ability evaluation and the application progress of BP neural network, combined with the actual situation of university innovation and entrepreneurship, this paper constructs the innovation and entrepreneurship evaluation index, uses BP neural network to build the evaluation model, and uses genetic algorithm to optimize and improve the shortcomings of BP neural network. Then, the experimental analysis and application design are carried out. The results show that the improved algorithm is basically consistent with the predicted value, small error, and fast convergence. When it is used in the evaluation of innovation and entrepreneurship ability, quantitative analysis results can be obtained, which provides a certain reference for the development of enterprises., Competing Interests: The author declares that there are no conflicts of interest or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Shixiao Li.)

This paper aims to further solve the risks encountered by college students in the process of employment and entrepreneurship in rural areas and improve the success rate of employment and entrepreneurship. It presents a method of social environment analysis based on college students' rural employment and entrepreneurship. This method deeply understands the employment and entrepreneurship environment of college students, deeply analyzes and identifies the constituent elements of college students' rural employment and entrepreneurship environment, and takes the social environment of college students' rural employment and entrepreneurship as the research object. With the help of questionnaire survey and data analysis, the survey shows that, in the channel selection of job hunting in the future, up to 77.20% choose to apply for a job through job fairs, and 69.60% will use advertising media such as networks and newspapers; 26.50% will apply for jobs through the talent market; 31.30% will apply for a job through relatives and friends; 15.40% will apply for jobs through independent entrepreneurship. This paper constructs the evaluation model of entrepreneurship and employment environment from the perspective of subjectivity and puts forward the optimization strategy of entrepreneurship and employment environment from four fields: policy environment, capital environment, education environment, and cultural environment., Competing Interests: The author declares that there are no conflicts of interest., (Copyright © 2022 Wenguo Chai.)

In the huge number of online university education resources, it is difficult for learners to quickly locate the resources they need, which leads to "information trek." Traditional information recommendation methods tend to ignore the characteristics of learners, who are the main subjects of education. In order to improve the recommendation accuracy, a recommendation algorithm based on improved collaborative filtering model is proposed in this paper. Firstly, according to the student behavior data, consider the behavior order to create the behavior graph and behavior route. Then, the path of text type is vectorized by the Keras Tokenizer method. Finally, the similarity between multidimensional behavior path vectors is calculated, and path collaborative filtering recommendations are performed for each dimension separately. The MOOC data of a university in China are introduced to experimentally compare the algorithm of the article as well as the control group algorithm. The results show that the proposed algorithm takes better values in evaluation indexes, thus verifying that this algorithm can improve the effectiveness of innovation and entrepreneurship education resources recommendation in universities., Competing Interests: The author declares no conflicts of interest., (Copyright © 2022 Li Geng.)

The main strategic direction of promoting rural revitalization is to achieve high-quality rural development, maintain the dominant position of farmers, and promote common prosperity of farmers. In recent years, the sustainable development of economy has raised farmers' awareness of starting their own businesses, and their entrepreneurial enthusiasm has been heating up. Based on the empirical survey data of typical rural areas in the Zhejiang Province and structural equation model analysis technology, this paper makes an empirical analysis of the relationship among network embeddedness, entrepreneurial resources, and entrepreneurial ability. The results show that knowledge resources have a significant negative impact on farmers' inclusive entrepreneurial behavior. The dual embedment of social network and industrial network has a significant positive impact on migrant workers' access to knowledge resources and operational resources needed for entrepreneurship, and the entrepreneurial ability of migrant workers mainly depends on operational resources. Under the background of common prosperity, we should continue to implement the policy of supporting farmers' inclusive entrepreneurship and encouraging industrial and commercial capital to enter rural areas, and at the same time, provide rural areas with agricultural knowledge, agricultural skills, and market guidance to promote employment., Competing Interests: The authors do not have any possible conflicts of interest., (Copyright © 2022 Xiangmin Meng et al.)

In this paper, the IoT-based adaptive mutation PSO-BPNN algorithm is used to conduct in-depth research and analysis of the entrepreneurship evaluation model for college students and practical applications. This paper details the principle, implementation, and characteristics of each BP algorithm and PSO algorithm. When classifying college students' entrepreneurship evaluation based on BP neural network, because BP algorithm is a local optimization-seeking algorithm, it is easy to fall into local minima in the training phase of the network and the convergence speed is slow, which leads to the reduction of classifier recognition rate. To address the above problems, this paper proposes the algorithm of PSO optimized BP neural network (PSO-BPNN) and establishes a classification and recognition model based on this algorithm for college students' entrepreneurship evaluation. The predicted values obtained from the particle swarm optimization neural network model are used to calculate the gray intervals, and the modeling samples are further screened using the gray intervals and the correlation principle, while the hyperspectral particle swarm optimization neural network model of soil organic matter based on the gray intervals is established afterward; and the estimation results are compared and analyzed with those of traditional modeling methods. The results showed that the coefficient of determination of the gray interval-based particle swarm optimization neural network model was 0.8826, and the average relative error was 3.572%, while the coefficient of determination of the particle swarm optimization neural network model was 0.853, and the average relative error was 4.34%; the average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model were 8.79%, 6.717%, and 9.9%, respectively. The average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model are 8.79%, 6.717%, and 9.468%, respectively. In general, the entrepreneurial ability of college students is at a good level (83.42 points), among which the entrepreneurial management ability score (84.30 points) and entrepreneurial spirit (84.16 points) are basically the same, while the entrepreneurial technology ability is relatively low (82.76 points), and the evaluation results are further verified by the double case analysis method. The current problems encountered by university students in entrepreneurship are mainly the lack of practicality, which indicates that universities, industries, and national strategy implementation levels are not sufficiently focused and collaborative in entrepreneurship development to varying degrees., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Huaxiang Fu.)