Aiming at the problems such as slow detection speed, unstable accuracy and great destructiveness of traditional crucible thickness measurement methods, a deep learning based crucible thickness measurement algorithm was proposed. Firstly, a set of visual hardware platform was built, and the image acquisition system of crucible thickness was constructed. The images are then captured by an electron microscope carried by the robotic arm. Secondly, by calculating Brenner function value and drawing corresponding curve, using wavelet transform peak detection algorithm to calculate the peak value of Brenner function curve and determine the internal focus image. The convolutional neural network is used to extract the features of spots, realize spot recognition and detection, and count the number of spots to get a bimodal curve. The peak value of the spot curve is calculated by wavelet transform peak detection algorithm to determine the external focusing image; Finally, the crucible thickness was obtained by averaging the frame difference of the two internal and external focusing images and the moving velocity of the electron microscope. The experimental results show that the measurement error of this method is less than 1 mm. The proposed method can accurately measure the thickness of quartz crucible and has high reference value for crucible quality detection.