Automated contract review has limitations in adapting to diverse clauses dynamically and executing complex review rules accurately. Therefore， a Dynamic Rule generation and Neuro-Symbolic reasoning framework for contract Review （DRNS-Review） using Large Language Models （LLMs） was proposed. First， a dynamic rule generation method was designed， domain knowledge and contract context were integrated， and LLMs were used to generate natural language and symbolic review rules in real time. Then， a contract review mechanism based on neuro-symbolic reasoning was constructed to extract local facts automatically， rules were localized through symbolic matching， and explainable reasoning was completed by combining symbolic reduction with LLM semantic execution. Finally， reasoning outputs from multiple segments were aggregated， and a dual?track review report， comprising both structured and natural?language content， was generated. Experimental results demonstrate that DRNS-Review improves the risk identification F1 value by 13.0 and 8.3 percentage points， respectively， and improves the Risk Level Accuracy （RLA） by 13.3 and 8.7 percentage points， respectively， compared to baseline methods LLM-Direct and ManualRule-LLM. It can be seen that integrating dynamic rule generation with neuro-symbolic reasoning improves the accuracy and adaptability of contract review effectively， providing a generalizable technical pathway for intelligent compliance applications.