Abstract: Aiming at the problem of the imbalance in the recognition accuracy of different fruit types by different classifiers, a fruit recognition method based on the fusion of multi-classifier DS evidence theory is proposed. The study selected five fruits in the fruits360 data set on kaggle as the research object. First, the color, texture, and shape features of the five preprocessed fruit images were extracted, and three classifiers of BP neural network, K-means, and SVM were selected respectively. Combining the recognition results of the tested images on each classifier and the classification accuracy of each classifier for different fruits, construct the basic probability function（BPA） function, and use the DS evidence fusion rules to fuse the classifiers to the tested images Recognition. The test results show that the method has an average accuracy of 95.2% for the recognition of five kinds of fruits, and the overall standard deviation is 0.029 93. While improving the recognition accuracy of a single classifier, it also solves the problem of uneven recognition of various fruits by the classifier. The average accuracy of the identification of the 10 test sets is 93.5%, and the overall standard deviation is 0.055. This method is more accurate and stable in the identification of fruit types.