Intelligent big data visual analytics based on deep learning

In this paper, we first constructed a deep learning model, optimized the LSTM model to get the BiLSTM model based on the long and short-term memory network, and used the generative adversarial network to calculate the probability distribution of data. Then, the advantages of deep learning in intelligent big data visualization and analysis are explored from the dimensions of data preprocessing, dimension anchor layout, coordinate expansion and data analysis. Finally, the efficiency of the deep learning model is compared with that of other algorithms using indicators such as accuracy and recall, and the feasibility of this paper’s method is verified by empirical analysis using intelligent transportation data as an example. The results show that the model in this paper achieves an accuracy rate of 95.5%, the loss rate is stable at 0.2% to 0.4%, and the average running time is maintained at 20ms, which are all better than other models. The predicted and real values of traffic data for the Deep-STCL model using deep learning basically match, indicating that the deep learning model has obvious advantages in data visualization and analysis.