Abstract: The invention claims an engine surge fault prediction system and method based on fusion neural network model, belonging to the technical field of time series data prediction. The system comprises a prediction module, used for generating prediction time series with a specified length through 3D structure time series data of the engine; a feature extraction module, used for extracting the local features of the prediction time series, semantic relations among data, and overall trend features of the series; a classification module, used for judging if there is a surge fault according to the local features of the prediction time series, semantic relations among data, and overall trend features of the series.

Abstract: The invention discloses a multi-hop attention and depth model, method, storage medium and terminal for classification of target sentiments. In said model, the combined two-dimensional lexical features (matrix3) produced by the first convolution operation module are used in each hop of attention calculation module and the attention weight information is continuously transmitted to sublayers; before calculation in the last hop, the one-dimensional lexical features input are weighted (by lexical vector weighting module) in the model with the attention (the first attention calculation module) before convolution operation (the second convolution operation module), to generate the weighted combined two-dimensional lexical features (matrix4) to be used in the final attention calculation.