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This invention belongs to the field of cutting parameter optimization technology, specifically relating to a multi-objective cutting parameter optimization method, including: acquiring cutting parameters and machine tool spindle power signals, calculating cutting specific energy, and simultaneously acquiring surface roughness; constructing a BP neural network model, optimizing the connection weights and thresholds of the BP neural network model using the Harris Eagle optimization algorithm to minimize the fitness function, and establishing a cutting specific energy prediction model; constructing a surface roughness prediction model with the encoded variables of cutting parameters as input and surface roughness as output, and establishing a multi-objective cutting parameter optimization model; solving the multi-objective cutting parameter optimization model to obtain the Pareto non-dominated solution set, using the entropy weight method for comprehensive decision-making, and selecting the solution with the largest comprehensive evaluation value as the optimal cutting parameter combination. This invention, through methods such as the BP neural network improved by the Harris Eagle optimization algorithm, reduces machining energy consumption, improves surface roughness, and enhances overall machining performance.