In view of the problems that the existing fault diagnosis methods cannot identify the long-time dependence relationship and have insufficient precision when processing the observed data in the chemical production process with high dimensions and obvious dynamic characteristics, the long-term memory model is improved in this paper, and a classification model based on depth learning and attention mechanism is proposed, Then the model classification effect is verified and the model in this paper is compared with the model before improvement. Finally, the sample data is drawn and the distribution of feature vectors in two-dimensional space is output at each level of the model by t-sne algorithm. The experimental results show that the improved in-depth learning model can achieve a recall rate of 92.71% and an accuracy rate of 93.05% for fault classification, which are improved by 16.84% and 13.66% respectively compared with the model before the improvement. The learning effect on data characteristics is better, and it is more suitable for chemical data.