Short text classification is an important research problem of Natural Language Processing （NLP）， and is widely used in news classification， sentiment analysis， comment analysis and other fields. Aiming at the problem of data sparsity in short text classification， by introducing node and edge weight features of corpora， based on Graph ATtention network （GAT）， a new graph attention network named Node-Edge GAT （NE-GAT） by fusing node and edge weight features was proposed. Firstly， a heterogeneous graph was constructed for each corpus， Gravity Model （GM） was used to evaluate the importance of word nodes， and edge weights were obtained through Point Mutual Information （PMI） between nodes. Secondly， a text-level graph was constructed for each sentence， node importance and edge weights were integrated into the update process of nodes. Experimental results show that， the average accuracy of the proposed model on the test sets reaches 75.48%， which is better than those of the models such as Text Graph Convolution Network （Text-GCN）， Text-Level-Graph Neural Network （TL-GNN） and Text classification method for INductive word representations via Graph neural networks （Text-ING）. Compared with original GAT， the proposed model has the average accuracy improved by 2.32 percentage points， which verifies the effectiveness of the proposed model.

By removing irrelevant features from the original dataset and selecting good feature subsets， feature selection can avoid the curse of dimensionality and improve the performance of learning algorithm.In the process of feature selection， only the dynamically change information between the selected features and classes is considered， and interaction relevance between the candidate features and the selected features is ignored by Dynamic Change of Selected Feature with the class （DCSF） algorithm. To solve this problem， a Dynamic Relevance based Feature Selection （DRFS） algorithm was proposed. In the proposed algorithm， conditional mutual information was used to measure the conditional relevance between the selected features and classes， and interaction information was used to measure the synergy brought by the candidate features and the selected features， so as to select relevant features and remove redundant features then obtain good feature subsets. Simulation results show that， compared with existing algorithms， the proposed algorithm can effectively improve classification accuracy of feature selection.