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Random forest algorithm is a common classification method. However, if the weights of many attributes in a data set are not same or close to each other, the direct use of this algorithm for data training will lead to the neglect of the interrelationships between these attributes, and it is difficult to reflect the differences brought by different weights of different attributes. Worse, if the number of attributes in the data set is relatively large, many attributes will be given very little weight when normalization is satisfied, which will also lead to information loss. All of these will have a negative impact on the final result. To solve these problems, this paper proposes an algorithm combining random forest classification and fuzzy comprehensive evaluation, which not only take into account the correlation between attributes in data training, but also retain the information in the original data set to the maximun. At the same time, this algorithm significantly improves the accuracy of random forest training results. &nbsp