Disclosed is a method for optimizing an open-pit mine production plan based on time-series prediction of copper price. The method includes collecting historical copper price based on dates, generating a processed multi-factor dataset using linear normalization and linear interpolation manners, and generating a correlation matrix, focusing on the correlation matrix using a Graph Convolutional Network (GCN) model, and decomposing a trend item sequence and a seasonal item sequence through Autoformer mechanism, predicting copper price through a time-series prediction model, and importing copper price as parameters into a production plan mathematical model to generating the production plan. The present disclosure is capable of focusing on the correlation between different factors in a multi-factor dataset including historical price information, effectively improving the accuracy of copper price prediction, and obtaining solutions with a higher accuracy and better fit for an actual production situation.