<p>Traditional network intrusion detection methods have problems such as low detection efficiency and poor accuracy, and are difficult to effectively deal with increasingly complex network attacks. Blockchain technology can ensure the authenticity and integrity of network data, prevent data from being tampered with or forged, and at the same time, through smart contracts, achieve automated intrusion detection and response mechanisms, providing new ideas for network security protection. This paper studies the application of blockchain technology in network data security, including data encryption, identity authentication and access control, etc. It proposes a network intrusion detection model that combines blockchain and deep learning. The model utilizes the distributed ledger feature of blockchain to ensure the authenticity and integrity of network data. And through smart contracts, an automated intrusion detection and response mechanism is realized. By introducing deep learning technology, a detection model capable of automatically learning the features of network data was constructed, and an incremental learning method was adopted to enable the model to be updated in real time to adapt to the constantly changing forms of network attacks. Through real-time data stream processing technology, real-time detection and early warning of network data have been achieved. Through experimental verification in a real network environment, the algorithm proposed in this paper can effectively detect network intrusions, featuring high accuracy and real-time performance. Compared with traditional methods, this algorithm significantly improves the detection efficiency and accuracy, while reducing the false alarm rate. The experimental results show that the combination of blockchain technology and deep learning has significant advantages in network intrusion detection and can effectively enhance the security and reliability of network systems.</p>