针对现有的专家推荐算法忽略了用户评论中蕴含的情感表达对专家专长表征的影响，从而导致推荐准确度不高的问题，提出基于双向编码器表示-多头注意力机制（bidirectional encoder representations from transformers-multi-head attention, BERT-MHA）的深度语义增强专家推荐算法。该算法基于预训练BERT模型，融合MHA机制，自动调整用户评论对专家历史回答问题的情感注意力权重，获取专家动态专长表征，并与静态专长联合以实现专家特征文本的语义增强，表征专家综合专长；通过注意力机制识别用户问题特征；采用多层感知机建模专家综合专长与用户问题间的非线性交互，预测推荐专家的匹配度。利用好大夫网站（haodf.com）的数据进行了不同参数配置及不同算法的对比实验，实验结果表明该算法在准确率（accuracy, ACC）和曲线下的面积（area under curve, AUC）指标下明显优于其他算法，能有效提高在线问答社区的专家推荐准确度。

In order to solve the problem that existing expert recommendation algorithms ignore the influence of the emotion expression contained in user comments on expert expertise representation, which leads to low recommendation accuracy, a deep semantic enhancement algorithm for expert recommendation based on bidirectional encoder representations from transformers-multi-head attention(BERT-MHA) was proposed. The algorithm was based on the pre-trained BERT model, incorporating the MHA mechanism to automatically adjust the emotional attention weights of user reviews towards questions the experts have answered before, capturing dynamic expertise representations. These representations were then combined with static expertise to semantically enhance the expert feature texts, representing the experts' comprehensive expertise. The algorithm identified the characteristics of user questions through the attention mechanism and employed a multi-layer perceptron to model the nonlinear interactions between the experts' comprehensive expertise and user questions, predicting the matching degree of recommended experts. Comparative experiments were conducted using data from haodf.com, with various parameter configurations and algorithms. The results indicate that the proposed algorithm significantly outperforms other algorithms under accuracy(ACC) and area under curve(AUC) indexes, effectively enhancing the accuracy of expert recommendations in online Q & A communities.