Abstract: A user interest-aware content replica optimized placement algorithm (UIARP) is proposed. Firstly, the interest subjects of the user-collective are extracted from their content access logs by clustering algorithms, and according to the weighting of the individual interest degree, their collective interest degree would be got and updated in real time; then under the nonlinear optimization model, replicas of larger collective interest degree have priority to be placed, with the goal of minimizing the average response time, which achieves the maximum match between placing replicas and users’ content demand. This algorithm not only ensures that users get interested replicas quickly, but also improves the system efficiency. From four aspects including average response time, the matching degree of request response, load balancing and the utilization rate of adjacent replicas, using 1-Greedy-Insert or others as compared algorithms, the simulation results show that each metric improves by 30% on average, which verifies the effectiveness of the proposed algorithm.