<p>With the rapid development of intelligent manufacturing and industrial Internet, the openness and complexity of cyberspace have exposed manufacturing systems to numerous security threats, undermining the integrity and confidentiality of production data, leading to production decision-making errors, and even paralyzing the entire production system. This paper analyzes the impact of computer network security on manufacturing automation systems and proposes a comprehensive solution that combines sensor fault diagnosis and network security protection. The secure transmission and storage of sensor data are ensured through Intrusion detection systems (IDS), encrypted communication protocols and identity authentication mechanisms. The fault detection algorithm is adopted to monitor the sensor data in real time and identify and locate sensor faults. Based on the collected production data and environmental data, a green energy intelligent decision-making model is developed by using machine learning and optimization algorithms to achieve intelligent management and optimal allocation of energy. The effectiveness of the proposed method was verified by constructing a simulated manufacturing environment. The experimental results show that this system can effectively identify different types of sensor faults, accurately alarm and provide repair suggestions. The intelligent decision-making model for green energy has demonstrated significant advantages in optimizing energy allocation and reducing production costs, thereby enhancing overall energy efficiency. By enhancing the security protection of computer networks, the system can resist various network attacks, ensure the integrity and confidentiality of data, and guarantee the stable operation of the production process.</p>