Particle-reinforced copper matrix composites exhibit good force-electric performance, but the quantitative relationship between the characteristic parameters of the reinforcement and the force-electric performance is difficult to quantify. To establish the relationship between the TiB and TiB2 reinforcement and the force-electric performance of copper matrix composites, greatly improve the strength of the copper matrix and control the change in conductivity within an acceptable range, a back propagation(BP) neural network and ant colony optimization based copper matrix composite performance prediction model (ACO-BP-Cu) was proposed. The relationship between the performance of the copper matrix composites and characteristic parameters was determined via a back propagation neural network, and the model structure was determined via global optimization of ant colony algorithms. The results show that the ACO-BP-Cu model can effectively predict the performance of copper matrix composites according to the characteristic parameters of TiB and TiB2, and have higher accuracy and stability compared with 9 regression algorithms including decision tree, linear regression, and K-nearest neighbor algorithms.