Abstract: In order to accurately identify cable force in complex boundary conditions， a new method of cable force identification using machine vision and generalized regression neural network （GRNN） is proposed. Machine vision technologies， such as the phase-based motion amplification algorithm and sub-pixel edge detection algorithm， are used to extract the vibration displacement time history data and identify the frequency through the cable vibration video to realize multi-point non-contact synchronous measurement of cable vibration deformation. A sample dataset is generated using the finite difference method. The smoothing factor of GRNN is obtained by the sparrow search algorithm （SSA）， and a SSA-GRNN cable force prediction model is constructed， establishing the correspondence between frequencies and cable force under complex boundary conditions. The obtained frequency information is input into the model for cable force recognition. Taking a single cable as an example， the numerical simulation of the cable in complex boundary conditions and the cable test under artificial excitation condition are carried out. The results show that the cable force identification using machine vision and GRNN can accurately identify frequencies through vibration video， and improve the recognition accuracy of the cable force in complex boundary conditions.