In this paper, machine learning and artificial intelligence are applied to art research to improve the intelligence of art research. This study concludes that the Gaussian homomorphic filter has the best processing impact in the image homomorphic filtering stage by comparing the processing effects of Gaussian homomorphic filter, Butterworth homomorphic filter, and exponential filter. Moreover, this study uses median filtering to eliminate salt and pepper noise in images and designs and implements a rapid digitization system for art works based on three-dimensional reconstruction. In addition, in order to improve the time efficiency of the system and the digital quality of art works and optimize the SIFT feature matching speed, this study proposes a relative position-based SIFT feature fast matching algorithm. Finally, this study verifies the effectiveness of the method proposed in this study through experimental research. The results show that it has a certain effect on promoting the development of art research and the application of computer technology in the art industry.