基于图神经网络的社交推荐模型在提升推荐系统性能方面有不错的表现。但现有方法都忽略了被查询的目标用户和项目节点与其邻居节点间可能存在特征不匹配的问题，导致噪声的引入而降低了模型性能。为了解决该问题，文中提出一种社交推荐模型DNSSR。首先构建一个包含用户和项目多元关系的关系图谱，图节点间信息关联更丰富；然后利用动态邻域采样机制获得与目标查询对的特征更一致的邻居节点，减少了噪声信息；另外，为了进一步提高模型预测性能，设计了一种增强型图神经网络对采样后得到的关系子图进行建模，它可以区分不同邻居节点的重要性并选择更可靠的信息源，获得更鲁棒的用户和项目嵌入向量用于评分预测。实验结果表明：相比其他先进模型，该模型预测误差明显降低，证明了文中所提各项方法的有效性；尤其是动态邻域采样机制，若将其弃用，DNSSR在Ciao数据集上的RMSE（均方根误差）和MAE（平均绝对误差）指标将分别上升6.05%和7.31%，在Epinions数据集上则分别上升3.49%和5.41%，充分验证了其能有效降低噪声干扰、提高社交推荐模型的性能。

The social recommendation model based on graph neural network has achieved good performance in improving the performance of the recommendation system. However, the existing methods ignored the possible feature mismatch between the queried target users and content nodes and their neighbors, which leads to the introduction of noise and reduces the model performance. To solve this problem, this paper proposed a social recommendation model DNSSR. Firstly, it constructed a relational graph containing multiple relationships between users and items, with richer information associations between nodes in the graph. Then the dynamic neighborhood sampling mechanism was used to obtain neighbor nodes that are more consistent with the characteristics of the target query pair, reducing noise information. In addition, in order to further improve the predictive performance of the model, this paper designed an enhanced graph neural network to model the sampled relationship subgraphs. It can distinguish the importance of different neighboring nodes and select more reliable information sources to obtain more robust user and item embedding vectors for rating prediction. The experimental results show that the prediction error of this model is significantly reduced compared to that of other advanced models, proving the effectiveness of the methods proposed in the paper. Especially for the dynamic neighborhood sampling mechanism, if it is abandoned, the RMSE and MAE indicators of DNSSR on the Ciao dataset will increase by 6.05% and 7.31% respectively, and the Epinions dataset will increase by 3.49% and 5.41% respectively, which fully demonstrate their effectiveness in reducing noise interference and improving the performance of social recommendation models.