Construction of machinery cost prediction model for green manufacturing and improved BP

In order to solve the problem that the accuracy of machinery remanufacturing cost prediction is not high, a machinery remanufacturing cost prediction model based on improved BP neural network was proposed. Firstly, the key influencing factors were identified based on the cost composition of mechanical product construction. Then, on the basis of the traditional BP neural network, particle swarm optimization algorithm was introduced to construct a machinery remanufacturing cost prediction model based on improved BP neural network. Finally, the constructed model was used to accurately predict the machinery cost. Experimental results show that the maximum error value of the relative error of the final result predicted by the model is 8.73%, the accuracy of the prediction result is high, the generalization ability is strong, the convergence speed is fast, and the training time is shorter. It is proved that the improved machinery remanufacturing cost prediction model based on BP neural network is feasible in practice.