In recent years, protein function prediction has made great breakthroughs in prediction performance. However, traditional protein function prediction methods mainly focus on single-species data, overlooking multi-species functional associations, which limits their generalizability to poorly annotated species. In addition, current deep learning methods are difficult to provide prediction basis in biology, which affects the application value in practical research. To solve the above problems, we propose a new interpretable multi-species protein function prediction model E-MSNGO, which constructs a heterogeneous network combining species and sequences by integrating sequence, structure and protein interaction (PPI) network information, and uses the graph attention mechanism in the model to efficiently propagate information. In addition, by calculating the functional similarity of proteins between species, the biological rationality of the prediction is improved, and the corresponding features are explained. Experimental results show that E-MSNGO has improved the corresponding indicators in multi-species protein function prediction, and can provide reasonable biological explanations, which improves the functional prediction ability of low-annotation species.