Focused on the issue that the attribute reduction algorithm based on neighborhood rough set only considers the influence of a single attribute on the decision attribute, and fails to consider the correlation among different attributes, a Neighborhood Rough Set attribute reduction algorithm based on Chi-square test (ChiS-NRS) was proposed. Firstly, the Chi-square test was used to calculate the correlation, and the influence between the related attributes was considered when selecting the important attributes, making the time complexity reduced and the classification accuracy improved. Then, the improved algorithm and the Gradient Boosting Decision Tree (GBDT) algorithm were combined to establish a classification model and the model was verified on UCI datasets. Finally, the proposed model was applied to predict the occurrence of microvascular invasion in hepatocellular carcinoma. The experimental results show that the proposed algorithm has the highest classification accuracy on some UCI datasets compared with the reduction algorithm without reduction and neighborhood rough set reduction algorithm. In the prediction of microvascular invasion in hepatocellular carcinoma, compared with Convolution Neural Network (CNN), Support Vector Machine (SVM) and Random Forest (RF) prediction models, the proposed model has the prediction accuracy of 88.13% in test set, the sensitivity, specificity and the Area Under Curve (AUC) of Receiver Operating Curve (ROC) of 88.89%, 87.5% and 0.90 respectively are the best. Therefore, the prediction model proposed can better predict the occurrence of microvascular invasion in hepatocellular carcinoma and assist doctors to make more accurate diagnosis.