This paper investigates unmanned aerial vehicle (UAV) path planning for data collection from ground-constrained sensors in large-scale industrial environments. Initially, clustering of nodes is performed to reduce the number of data access points. To address issues such as improper setting of cluster heads and data loss during data transmission among sensor nodes, a Dynamic Cluster Head Selection algorithm based on K-Medoids (DSK-Medoids) is proposed in this paper. This algorithm effectively tackles communication quality issues and enhances data collection. Additionally, the introduction of Deep Q Network (DQN) for path planning overcomes the “curse of dimensionality” problem associated with traditional path planning algorithms. This enables intelligent path planning strategies and improves UAV flight paths and data transmission efficiency. Through systematic simulation experiments and performance analysis, the effectiveness and performance advantages of the proposed methods are validated.