The category of e-commerce data is of great significance for its analysis. The classification based on human resources cannot adapt to the massive e-commerce data nowadays, and the classification based on traditional algorithm models can hardly extract valuable artificial features. In this study, the BiLSTM model integrated with an attention mechanism is introduced to classify e-commerce data. The model includes embedding layer, BiLTM layer, attention mechanism layer, and output layer. The embedding layer loads the word vector trained by Word2Vec; the BiLSTM layer captures the context of each word; the attention mechanism layer allocates weights for each word to synthesize new sample features. The experimental results show that the classification accuracy of the attention mechanism based on the inverse class frequency reaches 91.93%, which is improved compared with the BiLSTM model without the attention mechanism and other attention mechanisms introduced. This model has a good effect in the classification of e-commerce data and points out a new thinking direction for the introduction of attention mechanisms.