The stone cultural relics have been weathered and cracked for many years and are in urgent need of repair. To address this issue, presents a defect location algorithm for stone cultural relics based on defect feature enhancement and convolutional neural network (CNN) in view of the deficiency of depth learning in the application of crack detection for cultural relics. The algorithm extracts features through wavelet packet decomposition and selects an effective frequency band containing rich feature information as the input of the CNN network. Through model training and waveform classification recognition, it narrows the location range and improves the generalization ability and recognition rate of the crack location algorithm. An ultrasonic detection platform is established with field programmable gate array ( FPGA) as the core, and experimental verification is implemented on a cube specimen with a side length of 40 cm. The experimental results show that the accuracy rate of waveform identification is 11. 3% higher than that of traditional algorithms, and the average positioning error is less than 10% , which provides a reliable basis for defect detection of stone cultural relics and is helpful for the protection and restoration of cultural relics.