CPUE (catch per unit effort) standardization is an essential task in fisheries stock assessment and GLM (generalized linear model) which has been used as a standardized method in the CPUE standardization. Before using GLM, the error structure, independent variables, and interaction between variables in the model had to be assigned and it would cause a great error if the assumption was wrong. Moreover, GLM could not be used to handle missing values automatically and to detect and extract complex interactions from the CPUE data. Outliers also had a great impact on the results estimated by using GLM. In contrast to GLM, regression trees may do a great job to deal with the above situations. In this paper, based on simulation data and chub mackerel (Scomber japonicus) catch and effort data from Chinese lighting-purse seine fishery in the East China Sea and Yellow Sea, we compared the performance of the regression tree and GLM in the CPUE standardization and the results showed that both models could do a good job if there were no outliers in the data and nonlinear relationships between nominal CPUE and abundance. Because the regression tree was characterized by a step function, the GLM was better in standardizing CPUE in this situation. However, if there were outliers and nonlinear relationships, the regression tree would harvest less root mean square errors and explain more deviations with fewer variables than GLM. The regression tree also could detect the complex relationships between independent variables and response variables by visualization which was ideally suited to explore and analyze the catch and effort data from fisheries.