A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention

This article addresses the challenges of exploring potential patterns and modeling contextual dependencies in complex sequence data. By integrating short-term bidirectional memory (BiLSTM) with a multi-scale attention mechanism, a sequential pattern extraction algorithm has been proposed. BiLSTM sequentially captures forward and backward dependencies, improving the model's ability to perceive the structure of the overall context. At the same time, the Multi-Scale Attention Module assigns adaptive weights to key areas under different window sizes. This improves the model's responsiveness to important local and global information. In-depth experiments were conducted on publicly accessible multivariate time series data sets. The proposed model was compared to several common methods of sequence modeling. The results show that it outperforms existing models in terms of accuracy and recall. This confirms the efficiency and robustness of the proposed architecture in complex mode recognition tasks. Further ablation studies and sensitivity analyses were performed to study the effect of attention force tables and length of input sequences on model performance. These results provide empirical support for structural optimization of the model.