To solve the problem that the traditional algorithms, such as Support Vector Machine (SVM) and random forest, cannot make full use of the texture features and optical parameters of satellite images, a method of cloud/snow recognition based on Multi-dimensional multi-grained cascade Forest (M-gcForest) was proposed. Firstly, according to the difference between single-spectral and multi-spectral images, SVM, random forest, Convolution Neural Network (CNN), and gcForest (multi-grained cascade Forest) were selected to recognize cloud and snow on single-spectral satellite images, by quantitatively analyzing the performance of each algorithm on single-spectral images, CNN and M-gcForest were selected for multi-spectral cloud/snow recognition. Finally, improved M-gcForest was used to predict on HJ-1A/1B multi-spectral satellite images. The experimental results show that compared with CNN, the test accuracy of the M-gcForest on the multi-spectral dataset is increased by 0.32%, the training time is reduced by 91.2%, and the testing time is reduced by 53.7%. Therefore, the proposed algorithm has practicability in real-time and accurate snow disaster monitoring tasks.