The implementation of intensive control of locomotive cables mainly relies on conventional PID algorithms, and the control parameters applied in this process are fixed, resulting in a large overshoot of control results in dynamic environments. Therefore, a new intensive automatic control system for locomotive cables is proposed. In terms of hardware, design for machine vision detection devices and PLC controllers. In terms of software, consider the constraints faced during the intensive work process and determine a reasonable cable intensive layout plan. According to this plan, intensive processing will begin, and during the process, machine vision detection devices will be used to collect cable cross-sectional images. Through edge detection and feature extraction, the current cable status will be detected. Regard the detected state and expected state as input variables, import them into a fuzzy neural PID control architecture with parameter adaptive updating characteristics, and achieve intensive automatic control. The test results show that the overshoot exhibited by the system control results is only 0.89%, demonstrating good performance in intensive automatic control.