The existing edge server deployment algorithms predominantly focus on the locations of base stations, often overlooking user experience. To address this limitation, this study proposes an edge server placement algorithm based on spectral clustering and Q-learning (QSC). The algorithm not only considers the locations of base stations but also incorporates the number of users and the geographical positions of base stations, with the aim of achieving a balanced workload distribution across edge servers while minimizing the average user access latency. The process begins with using spectral clustering (SC) to determine initial cluster centers, followed by applying the Q-learning algorithm to refine these centers, which are then designated as the deployment positions for the edge servers. Experimental results demonstrate that, compared to the traditional K-means algorithm, the QSC algorithm reduces access latency by 10.04% and enhances workload balancing by 34.33%. Overall, the QSC algorithm exhibits superior performance in terms of both access latency and workload balancing.