During the electrolytic copper refining process, the copper content in the anode plate is crucial to the electrolysis efficiency. Fifteen elemental qualities of mixed copper concentrate and crude copper are taken as independent variables, while the copper element quality of the anode plate is considered as the dependent variable. The maximal information coefficient (MIC) was used to analyze the nonlinear correlations among the elements in 54 representative test datasets. The study found that the arsenic (As) content in the mixed copper concentrate and the antimony (Sb) content in the purchased crude copper had the highest correlation with the copper content in the anode plate, with MIC values of approximately 0.8228 and 0.8362, respectively. Based on these findings, a whale optimization algorithm-optimized least squares support vector machine (WOA-LSSVM) regression prediction model was constructed to predict the copper element quality of the anode plate. Experimental results indicated that the WOA-LSSVM model has a high prediction accuracy, with an R2 value reaching 0.9245 and a low root mean square error (RMSE). The prediction accuracy of the WOA-LSSVM hybrid model for anode plate copper content was 4.45% to 123.05% higher than that of other models. Nonlinear analysis methods can effectively capture the complex relationships between different factors in the production process of anode copper. Combining nonlinear analysis methods with machine learning techniques can improve the timeliness and adaptability of anode copper quality control.