To address the current problems of combining single domain-specific knowledge and poor fusion in suicidal ideation detection tasks, this paper proposes a Multi-Head Knowledge Attention Mechanism model that fuses domain knowledge (DK-MHKA) to fully integrate the suicide risk severity lexicon and the user's neurotic personality traits. The model involves integrating suicidal tendencies attributes into the semantic domain that encompasses the user's social media content, with the aim of enhancing the model's linguistic representations. Furthermore, the method employs a multi-head knowledge attention mechanism to effectively combine various sources of features, resulting in an enhanced predictive capability of the model. The experimental findings indicate that the suggested DK-MHKA model outperforms alternative baseline models in terms of forecasting precision. Additionally, the ablation experiments confirm the individual contributions of each module to the overall performance of the model.
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