Research and Application of the BMHA Model for Text Similarity Calculation Integrating BiLSTM and Attention Mechanism

YU Yan; CHEN De-tao; HE Wei; JI Wei-dong; NAN Shu-yin; FAN Xue-qin; ZHAO Wei

Computer & Telecommunication ›› 2026 ›› Issue (2) : 17-23.

YU Yan, CHEN De-tao^*, HE Wei, JI Wei-dong, NAN Shu-yin, FAN Xue-qin, ZHAO Wei

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Abstract

In the C language knowledge question answering system, text similarity calculation is very important, but traditional methods, such as cosine similarity and editing distance, have problems such as inability to deeply understand semantics and difficulty in dealing with language complexity. To this end, this study proposes a C-knowledge text similarity calculation model (BMHA) that fuses bidirectional long short-term memory network (BiLSTM) and attention mechanism. The model takes advantage of the context-aware ability and attention mechanism of BiLSTM to focus on key information to dig deep into text semantics. Experimentally, on the platform built based on the TensorFlow framework, the F1 value of the BMHA model in the semantic matching task is 92.1%, which is 19.7% and 24.5% higher than that of the traditional LSTM and CNN benchmark models, respectively. It shows that its performance is excellent, which provides a new method for text similarity calculation in the field of programming education, and is expected to promote the development of related fields.

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YU Yan, CHEN De-tao, HE Wei, JI Wei-dong, NAN Shu-yin, FAN Xue-qin, ZHAO Wei. Research and Application of the BMHA Model for Text Similarity Calculation Integrating BiLSTM and Attention Mechanism[J]. Computer & Telecommunication. 2026(2): 17-23

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