In the context of the rapid development of the Internet, the issue of network security has become increasingly complex. Threats such as malware, cyberattacks, and data breaches are on the rise, and traditional security approaches are challenging to address new threats, especially in the face of large-scale and highly sophisticated attacks. In order to solve this problem, big data analysis technology can be used to collect and analyze network data, including traffic, logs, and abnormal behaviors, so as to discover potential security threats in time. In this challenge, machine learning algorithms play a key role, including deep learning, support vector machines, and decision trees. The automation of the intelligent protection system reduces the burden of manual intervention and improves the efficiency of security incident handling. According to the experimental data, the protection success rate of the intelligent network security protection technology system based on big data and machine learning is 99.10%, the protection failure rate is 0.9%, the BUG rate is 0.72%, and the information feedback accuracy rate is 99.53%, which is the best among the convolutional neural network and traditional protection technology. Moreover, the adaptability and evolution of the machine learning model enables it to continuously learn and adapt to new attack methods, laying the foundation for the construction of a more intelligent and adaptive network security protection system, and providing a more powerful security guarantee for the network system.