With the increasing competitiveness of today’s job market, job recommendation systems play a crucial role in matching job seekers with suitable opportunities. However, current approaches often struggle to capture the complex relationships between users and job postings due to data sparsity, limited contextual understanding, and difficulties in extracting user preferences from long texts like resumes. To address these limitations, we propose a novel approach that combines a heterogeneous Graph Convolutional Network (HGCN) with Latent Dirichlet Allocation (LDA). While traditional methods fail to model the diverse nature of user preferences and job characteristics, our approach combines the ability of HGCN to capture intricate user-job interactions with the capability of LDA to extract latent topics from job descriptions and resumes. This hybrid approach results in a richer representation of both relational and semantic information, enabling more personalized and accurate job recommendations. Experimental results confirm that our model significantly outperforms existing methods, demonstrating its effectiveness in enhancing recommendation accuracy.