Recognition method of CT images based on color, morphology, and texture is inaccurate and unreliable recently. Therefore, a medical image recognition method based on data mining is proposed with a multi-feature fusion in this paper. First, image acquisition method is determined by analyzing the environment of CT image recognition. Then, acquired CT image is standardized and whitened to reduce redundant information. Moreover, based on feature of the color, texture, and height in preprocessed CT images, a deep neural network is trained by using a large amount of image data in normal scene. The deep learning classifier is fine-tuned by using the marked multi-feature CT image data. Finally, output recognition result is obtained according to the classification decision threshold. Experimental results show that the correct recognition rate of the proposed method can reach more than 98%. The accuracy rate is higher and the stability of proposed method is better by comparing with traditional CT image recognition methods. KEYWORDSCT images, data mining, deep learning, image recognition, multiple feature INTRODUCTIONIn recent years, with the rapid development of medical imaging technology, it improved to big medical image analysis. Today, many big database are constructed. How we can extract useful information from massive medical image data in the database has brought great challenges to medical image recognition since traditional database technology cannot provide enough service for the large database. Data mining is used to obtain effective information in medical image database. CT image recognition is a multidisciplinary cross-domain of comprehensive medical imaging, mathematical modeling, and computer technology. 1,2 Today, there are two new issues that are brought because of large scale of multi-feature CT image. One issue is CT image data that has a higher dimension and requires a model with stronger learning adaptability. The other is big CT image data that is more fragmented with more complex data structure, which requires integration of huge different information. 3 Such requirements cannot be solved by traditional data analysis methods. Therefore, how we extract useful information from massive CT image data has become a research hotspot. With the rapid development and popularization of CT imaging equipment, CT image data have increased explosively. How we can efficiently and accurately analyze CT image has become a major challenge. 4 Furthermore, computer-assisted CT medical image analysis improves the efficiency and accuracy of the analysis and diagnosis of CT image. Therefore, computer-aided detection and diagnosis are becoming a cross-sectional research area that receives more and more attention.A multi-feature CT medical image recognition method was proposed based on polarization spectrum. 5 The method used indoor imaging spectrometer FISS-P equipped with polarizers to collect the polarization spectrum images, including general images and five kinds of CT medical images indoors. They recognized CT images by ...