Abstract: In order to reduce difficulty of Chinese bee image recognition process caused by interference factors of different light intensities and different contrasts，a bee image recognition method based on combination of Gabor filtering，PCA dimensionality reduction and SVM was proposed.The research was to perform grayscale and normalization processing on artificially collected Chinese bee images，and use Gabor filter technology to extract image features of processed pictures，and further use PCA to linearly reduce high-dimensional feature vectors，and finally the pictures.The matrix was classified and identified through SVM with different kernel functions.The SVM with different kernel functions was used for comparative modeling，and modeling time，recognition accuracy，and image recognition time of the Chinese bee image after feature extraction were tested and analyzed.This experiment showed that the image of Chinese bee was extracted by Gabor feature and reduced by PCA.The feature matrix obtained through SVM with the kernel function of Sigmoid has the best recognition characteristics.