Conviction in judicial cases is an important part of judicial business

but there is still a lack of effective intelligent auxiliary tools and methods. Aiming at the difficult problem of conviction in judicial cases

a method combining tensor decomposition and Bi-LSTM neural network is proposed. This method represents the case data as a tensor and introduces a template tensor in the tensor decomposition process. The template tensor can be continuously optimized during the training process of Bi-LSTM neural network classification model

so that the decomposed core tensor contains more effective tensor structure and feature information

which is helpful to improve the accuracy of the subsequent classification model and realize the accurate conviction in judicial cases. The experimental results show that the proposed method for conviction in judicial cases based on tensor decomposition and Bi-LSTM has better accuracy than the existing methods.