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In recent years, with the further development of deep learning and high-performance computing technologies, more intelligent algorithms have been applied to the actual production processes. The main research content of this paper is the prediction of element yield in the steel industry. First of all, considering that there is a lot of noise in the original data, it will affect the prediction accuracy, so this paper uses the "3-σ" principle and wavelet threshold denoising to preprocess the original data. Subsequently, this article starts from the effect of influencing factors on the yield, and uses a convolutional neural network (CNN) to predict the yield. By analyzing the prediction results, it is found that the prediction effect of CNN for some samples is not good. In order to optimize the model, this article starts from the two perspectives of the model and the algorithm. On the one hand, the LSTM neural network is introduced to take the historical data information of the yield into account, on the other hand, the CNN network and the LSTM are integrated through the Adaboost method. The advantages of the network are combined to establish an integrated learning algorithm of CNNLSTM-Adaboost, which further improves the prediction accuracy of the model. By simulating the model, a high-precision prediction result is obtained.

With the continuous development of the national big data strategy and the construction of "Digital China", cities have been given new connotations and requirements. This paper takes the real estate price changes in Hainan Province as the background, collects relevant information and data, and makes relevant analysis and reasonable predictions for the housing price changes in Hainan Province. This paper comprehensively considers the prediction defects of MEA_BP neural network, Elman neural network and Wavelet neural network, and uses a combination algorithm based on entropy method to comprehensively consider the results of all prediction models. On the one hand, it can improve the prediction accuracy of the model, and on the other hand, it can reduce the sensitivity of the prediction results to changes in a certain factor, making the prediction results more reliable. This paper establishes a multiple and effective new era smart city prediction model, which provides a reference for building a new city database supported by a new generation of information technology.

With the escalation of the trade war between China and the United States and the outbreak of the new coronavirus epidemic in early 2020, China’s economy has been seriously affected. Accurate prediction of future economic development is a necessary means to formulate economic development strategies. For this reason, taking Guangdong province, the province with the strongest economic vitality, as an example, this paper uses multiple regression method to calculate the correlation degree and influence weight of population number, number of enterprises and per capita disposable income with economic development, and obtains multiple regression linear equation. In addition, this paper also uses the NAR dynamic neural network model in machine learning algorithms to predict the future trends of the three factors and economic aggregates, and analyzes the feedback results of network training errors, autocorrelation values, and partial correlation values. Compared with the multiple regression method, it is found that the final results of the two are very similar, with small errors and high correlation.