The recommendation algorithm based on the graph neural network generates the feature representation of nodes by obtaining knowledge from graphs, which improves the interpretability of recommendation results. Nevertheless, as the original data scale of the recommendation system continually expands, a large amount of text data containing semantic information has not been effectively used. Additionally, the graph neural network does not distinguish key nodes when fusing the neighbor information in the graph, making it difficult for the model to learn high-quality entity features, which in turn leads to a decrease in the quality of recommendation. This study combines the graph neural network with a semantic model and proposes a recommendation algorithm based on the graph neural network which integrates semantic information and attention. This algorithm processes entity-related text information based on the SpanBERT semantic model and generates feature embeddings containing semantic information. It also introduces the attention mechanism into the process of influence propagation and fusion based on user social relations and user-item interactions to effectively update user and item entity features. The comparative experimental results on public datasets show that the proposed method is better than the existing benchmark methods in all indicators.