Document Similarity Detection for Project Development Using Fused Interactive Attention Mechanisms

This study introduces a novel multi-feature fusion model aimed at improving text similarity calculation in scientific and technological projects. The primary objective is to enhance the accuracy and efficiency of assessing text similarities, particularly in evaluating originality and identifying duplications in project submissions. To overcome the limitations of traditional text similarity methods (e.g., Vector Space Models, Latent Dirichlet Allocation, and TF-IDF) in capturing complex semantic and structural features, a hybrid model is proposed. The model combines word embeddings (word2vec and cw2vec), a Bi-LSTM network, and a multi-perspective convolutional neural network (MP-CNN) for effective feature extraction. Additionally, a fusion attention mechanism and interactive attention are incorporated to improve the extraction of semantic, contextual, and structural information. Experimental evaluation on two benchmark datasets demonstrates that the proposed model achieves an average precision of 0.75, a recall of 0.71, and an F1-score of 0.73, outperforming traditional methods (LDA, TF-IDF, Word2vec+Cosine) and deep learning baselines (Siamese-LSTM, MP-CNN) by more than 10% on average. These results confirm that the proposed architecture effectively balances semantic relevance and structural integrity, yielding superior similarity detection performance. The integration of advanced deep learning components—Bi-LSTM, MP-CNN, and attention mechanisms—substantially improves both the accuracy and efficiency of similarity evaluation, providing a more reliable and objective approach for scientific project assessment.