<p>In the field of new media content distribution, with the increasing complexity and personalization of user needs, traditional rule-based content recommendation systems have gradually shown their limitations. To solve this problem, this study proposes a new media content distribution strategy based on fuzzy logic optimization, aiming to improve the efficiency and user experience of the recommendation system. By constructing a fuzzy reasoning system model, designing a fuzzy rule base, and implementing a defuzzification process, the strategy can handle uncertainty and multi-dimensional information to achieve more accurate and personalized recommendations. At the same time, the introduction of a reinforcement learning mechanism enables the system to have self-adjustment capabilities and continuously optimize the recommendation results based on real-time data. Experimental results show that the fuzzy logic optimization strategy is significantly better than the traditional method in key performance indicators such as click-through rate (CTR), distribution success rate, and system response time, especially during the user active period, the CTR improvement is particularly obvious. In addition, the correlation matrix analysis reveals the complex relationship between user feedback and recommended content attributes, providing a basis for further optimizing the recommendation algorithm. In summary, this study not only verifies the application potential of fuzzy logic in content distribution, but also points out the direction for the development of future intelligent recommendation systems.</p>