This paper studies missing value filling and compares the filling effects of five methods: Mean, KNN, Random Forest, GBDT, and Stacking under different missing proportions, proving the superiority of ensemble learning algorithms in filling performance when multiple feature values are missing. Then the missing value filling method of KNN+integrated learning is proposed to further improve the filling performance.