In recent years， researchers have proposed many improved model architecture designs for Graph Neural Network （GNN）， driving performance improvements in various prediction tasks. But most GNN variants start with the assumption that node features are equally important， which is not the case. To solve this problem， a feature selection method was proposed to improve the existing model and select important feature subsets for the dataset. The proposed method consists of two components， a feature selection layer， and a separate label-feature mapping. Softmax normalizer and feature “soft selector” were used for feature selection in the feature selection layer， and the model structure was designed under the idea of separate label-feature mapping to select the corresponding subsets of related features for different labels， and multiple related feature subsets were performed union operation to obtain an important feature subset of the final dataset. Graph ATtention network （GAT） and GATv2 models were selected as the benchmark models， and the algorithm was applied to the benchmark models to obtain new models. Experimental results show that when the proposed models perform node classification tasks on six datasets， their accuracies are improved by 0.83% - 8.79% compared with the baseline models. The new models also select the corresponding important feature subsets for the six datasets， in which the number of features accounts for 3.94% - 12.86% of the total number of features in their respective datasets. After using the important feature subset as the new input of the benchmark model， the accuracy more than 95% （using all features） is still achieved. That is， the scale of the model is reduced while ensuring the accuracy. It can be seen that the proposed new algorithm can improve the accuracy of node classification， and can effectively select the corresponding important feature subset for the dataset.