Aiming at the problem of water quality anomaly monitoring, a bio-monitoring method based on computer vision and support vector machine is proposed. First, fish behavior movement information is collected by computer vision. Then, establishing training sample set is used for obtaining water quality anomaly monitoring model. Finally, the model is utilized to analyze the fish data of unknown water quality. Kernel function type and parameter optimization have a significant impact on the model. The different types of kernel function experimental results are compared to choose the best kernel, and then Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) and the Grid Search method (Grid Search) are used to optimize parameter. The experimental results show that the method can monitor the water quality quickly and efficiently.