In this dynamic and increasingly complex network environment, security threats are constantly evolving. The longer the security assessment technology is applied, the more difficult it is to cope with dynamically evolving attack patterns. This paper studies a network security assessment strategy based on intelligent algorithms, aiming to improve the accuracy and real-time performance of security assessment. First, network traffic data and security event logs are collected, and data quality is ensured through data cleaning and feature extraction. Next, the RNN neural network is implemented for attack pattern detection, and a clustering algorithm is used to classify threats. Finally, reinforcement learning is introduced to achieve continuous updating of security policies and allocation of resources. This strategy improves the detection accuracy to 98.8%. Intelligent algorithms have obvious advantages in the field of security assessment and pave the way for security management in dynamic and highly complex network environments.