Recognition of weather phenomena based on images is essential for the analysis of weather conditions. To address the problems that traditional machine learning methods are difficult to accurately extract various weather features and poor in classifying weather phenomena and the accuracy of deep learning for weather phenomena recognition is not high, a weather recognition model based on image block and multi?headed attention mechanism is proposed. The model introduces Swin Transformer into the field of weather recognition for the first time, and adopts a multi?headed attention mechanism combining window multi?head self?attention layer and shifted?window multi?head self?attention layer, whose regionally relevant features extraction capability makes up for the shortcomings of traditional methods and can extract complex weather features from images. The model is trained using transfer learning, and the fully connected parameters of the fine?tuned model are input to the Softmax classifier to achieve recognition of multi?category weather images with 99.20% recognition accuracy, which is better than several mainstream methods in comparison, and it can be applied to ground weather recognition systems as a weather recognition module.

The COVID?19 epidemic is facing the influence of a variety of complex practical factors, which makes the development of the epidemic uncertain. In order to overcome the problem of large error in epidemic forecasting results due to the limitations of many ideal assumptions based on the infectious disease compartment model, a time series forecasting model based on deep learning is adopted to predict the epidemic development, and an informer model based on transformer model is established. Attention mechanism and distillation mechanism are applied to the time series forecasting of epidemic data. The threshold autoregressive (TAR) model and a variety of mainstream recurrent neural time series prediction models are used as comparison models. Through simulation experiments, the current number of remaining infections in the epidemic data of China, America and Britain is predicted in the short term, and RMSE and MAE are used as evaluation indicators, and then the best model is selected for medium ? and long?term prediction. The experimental results show that the indicator value of the informer model is optimal in both RMSE and MAE, further indicating that the prediction accuracy of the informer model is higher than that of other comparative models in China, America and Britain. Finally, the Informer model is used for the development of the epidemic in China，America and Britain medium and long?term prediction.

Due to the huge amount of the customers'data the rise of various financial products and the short?term impact of the epidemic, banks are facing with increasing pressure resulting in the business volume declined sharply. The traditional classification tree model can not make more accurate prediction of long?term deposits and carry out accurate marketing to customers according to customer information. Therefore, this paper proposes a three?layer neural network model. Through the experiment, the customer data of grape Island banking institutions are predicted, and compared with the prediction results of a traditional decision tree, random forest model, AdaBoost model and XGBoost model. The experiment shows that compared with the other four models, the neural network model has a better effect of prediction, the model evaluation AUC reaches 0.977 7 and the accuracy reaches 99.06%.

For a long time, researchers mostly analyze the transmission process of infected nodes in complex networks to get the target of forecasting and arresting the extend of the infectious diseases. In this article, the SEIR propagation dynamics model was extended to the undirected and powerless large small world network, and the weights between nodes were given as infection ability. Two initial node selection methods were selected to carry out multiple simulation experiments. Based on the traditional method of judging the impact of transmission by the number of infected people and infection threshold, the specific values of infection probability, peak value and inflection point time were added to analyze the impact of initial node selection on transmission process more comprehensively. The compared experimental results show that the initial node which the degree is larger and the betweenness is larger, the larger the propagation scale, the faster the propagation speed and the shorter the equilibrium time. This study provides some reference value for guard against and control of the extend of infectious diseases.