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As one of the important aquaculture species in China, river crabs are well-loved by consumers.In the process of river crab aquaculture, scientific baiting is a key factor to ensure the healthy growth of river crabs and improve aquaculture efficiency. By comprehensively analyzing the factors affecting the baiting amount of river crab aquaculture, an ensemble learning algorithm was used to establish a prediction model for the baiting amount of river crab aquaculture. A data collection system was set up to collect key parameters such as river crab biomass, crab population, sex ratio, water pH value, temperature, dissolved oxygen, and crab feeding amounts to establish a baiting data set;data preprocessing techniques were used to smooth and normalize the data set to reduce the interference of outliers on the prediction results, and at the same time to eliminate the influence of different scales of the characteristic data;the particle swarm optimization （PSO） algorithm was introduced to improve the ensemble learning and establish a baiting model for river crab culture. The particle swarm optimization algorithm was introduced to improve the ensemble learning, and the bait quantity prediction model was established to realize the accurate prediction of the bait quantity of river crab aquaculture. The results of practical application tests showed that the average absolute error （MAE） of this model was 0.349 71 g, the root mean square error （RMSE）was 0.491 14 g, and the coefficient of determination （R2） of key performance reached 0.903 58.