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Aiming at the problems that the features extracted by the traditional deep forest algorithm are not complete, and the equal-power decision mechanism is easy to produce differences in the classification results, an improved DeepForest (IDF) model applied to inexact graph matching is proposed. Firstly, in the process of mining feature subsets, the methods of fusing moving windows and random moving windows are adopted. While the moving window scans the sample, a same size feature subset of the moving scanning window is randomly captured, and these form a new feature subset, which is used as the input of the cascade module. Secondly, in the iterative process of the cascading forest, the weight of decision result in the current forest is calculated. Compared with the upper level forest, the weight value is assigned to the current forest by the strategy rule of Min, and iteration is continued until the result meets the given threshold value by the model. Finally, training and testing are conducted on datasets such as MUTAG, PTC and COX2. The experimental results show that, compared with traditional deep forest algorithm, IDF fully considers the structural characteristics of the graph, and can effectively enhance the diversity of samples and the goodness of fit, and reduce the decision-making difference and the complexity of the model. It efectively improves the classification and recognition rate of the model.