With the development of emerging technologies such as artificial intelligence, big data and 5G, the threats faced by enterprises are becoming more and more serious. According to relevant data, in the decade from 2015 to 2025, the global potential economic loss caused by cyber attacks may be as high as $294 billion. The escalation of network risk has made the government, enterprises and individuals pay more attention to the risk. With the adjustment of relevant national cyberspace policies and the acceleration of cyber military power construction, the cyberspace competition may set off a new climax. Countries will pay more attention to data security. Data governance has become an important national strategic resource and production factor. The problem of network attacks and data abuse against data is becoming more and more serious. It is urgent to improve the level of data security governance. The multi-party security awareness system based on artificial intelligence and large graph calculation aims to combine the traffic data and system log data in IDS system, and combine the graph method and artificial intelligence algorithm to identify intrusion behavior in an all-round, multi-dimensional and accurate manner, so as to protect the security of large-scale network system. The multi-party security awareness system based on artificial intelligence and large-scale computing contains three main functions, and these three functions complement each other to complete the burden of protecting the security of the system. Among them, the malicious traffic identification classification and prediction system is designed to find out potential network attacks through system traffic data, refine the classification of attacks and feedback the analysis results to security personnel at the first time, while the malicious traffic prediction function is designed to predict future events based on historical events, and to analyze the attack technologies used by past attackers to determine what kind of attack measures may continue to be taken in the future, so as to prepare in advance to do a good job of response and protection measures. The intrusion detection function aims to detect the intrusion behavior through the log data in the IDS system and feedback the analysis results to the defense party at the first time. Intrusion detection integrates three improved ML algorithms to analyze the network behavior, making the intrusion detection results more efficient, more accurate and more comprehensive. Attack path tracing and attack probability determination, as an important means of tracing the source of the attack, is an auxiliary and strengthening means of the first two system functions. The aim is to further analyze the attack path taken by the attacker through the graph method for the detected attack behavior, and analyze the common attack source as the source of the attack through a large number of paths. At the same time, the attack probability determination can assist the intrusion detection function to make more accurate intrusion detection. Our team is composed of 5 graduate students. We all have many years of experience in network security and artificial intelligence. We have won awards and published nearly 100 achievements in the field of network security. We have strong technical skills and aim to develop network security products with practical value to continuously protect the country's network security cause. In the future, we will not forget our first heart, keep in mind our mission, and continue to empower the national network security cause!