In order to improve the accuracy of hydraulic cylinder machining man-hour forecasting in metallurgical industry,a new man-hour forecasting method based on case-based reasoning and BP neural network optimized by hybrid Jaya optimization algorithm(CBR-HJaya-BP) was proposed.The hybrid Jaya optimization algorithm was used to optimize the initial weights and thresholds of BP neural network,a population initialization strategy based on Sin chaos and backward learning was adopted to improve the quality of initial solutions.The transfer factor in the Archimedes optimization algorithm was used to balance the process of exploration and development,the uniform crossover was used to generate the intermediate population in the exploration phase and the Jaya formula was used to generate the intermediate population in the development phase.Finally,the Metropolis criterion of simulated annealing algorithm was used to jump out of the local optimum.Taking the historical processing database of a hydraulic manufacturing enterprise as the sample,the case-based reasoning method was used to extract the historical data which was similar to the hydraulic cylinder needed predicting.Then the prediction experiments were carried out by using the proposed HJaya-BP model,the Jaya-BP model and the initial BP neural network model.The results show that the forecasting accuracy and stability of HJaya-BP are all the best.