Traditional cable coaxiality detection is based on X-ray machine image feature detection method, which can’t guarantee the detection accuracy quantitatively, has a small scope of application and poor anti-interference ability. In this paper, with the support of automatic image acquisition system, convolutional neural network (CNN) and line detection technology, a set of intelligent cable coaxiality detection method is proposed. Firstly, the collected cable images are intelligently classified by the trained neural network model; the Canny operator edge detection and Hough transform line detection parameters are adjusted according to different image categories to meet the detection requirements; the coaxiality of the cable is calculated according to the inner and outer diameter of the detection result. This method and process make full use of the advantages of automation and intelligence of machine learning algorithm. It is applied to the coaxiality detection of mineral insulated cable with complex production process. The success rate of CNN model classification reaches 96.87%, and the success rate of coaxiality detection reaches 94%, which can meet the technical requirements of real-time detection of enterprises.