The application of neural networks in the field of engineering machinery remanufacturing is of great significance for improving product quality, reducing production costs, and shortening component update cycles. This article focused on how to optimize neural algorithms. Firstly, the theory of artificial neurons, neural functions, and two basic methods were introduced. Subsequently, a comparative analysis was conducted between traditional artificial language programming methods and artificial intelligence technologies. In addition, by comparing and analyzing the problems existing in different models, relevant improvement plans were proposed. Finally, simulation experiments were conducted. The test results showed that the success rate of remanufacturing optimization technology assembly could reach 100%, and the running time was fast, which verified its effectiveness and reliability. This indicated that the assembly combination plan for remanufactured parts can be quickly determined to meet the assembly requirements of customers or enterprises, improving the efficiency and quality of remanufactured assembly. Neural networks can analyze a large amount of engineering machinery remanufacturing process data to discover fault modes and abnormal behaviors. By monitoring and predicting faults in the remanufacturing process of construction machinery in real-time, repair measures can be taken in advance to reduce the risk of faults occurring and the uncertainty of the remanufacturing process.