No abstract

To solve the problem that the explosive growth of data cannot be effectively analyzed, a big data analysis and prediction system based on deep learning is proposed. The systems proposed in this paper optimize the design of circulatory neural networks using an optimization processing strategy in addition to circulatory neural networks based on a distributed parallel processing framework that studies and analyzes disrupted neural networks during the in-depth study. The results are as follows: using the DCNNPS parallel strategy can effectively improve the training speed of the convolutional neural network to a small extent, and the speed can be improved by more than 50%. Compared with the other two, the network optimized by DCNNPS is less sensitive to data growth in the scenario of a large amount of data and is more suitable for processing a large amount of data. Moreover, with the increase of computing nodes, the acceleration effect is more obvious, the acceleration ratio is higher, and the improvement energy is about 70%. It is proved that the system proposed in this paper can process massive data very effectively and make a great contribution to the development of the industry.