<p>The accuracy of recommendation algorithms is key to improving expert fault repair systems. However, current prediction methods using single-feature graph convolution still face challenges in adequately representing the embeddings of expert attributes and repair components. Additionally, graph convolutional network (GCN)-based feature encoders struggle to capture the potential interactions between experts and repair components. Moreover, the direct combination of different types of graph features can lead to an imbalance in the contribution of each feature type to the recommendation model, ultimately affecting the effectiveness of the recommendations. This paper integrates recommendation systems from the e-commerce domain with the Industrial Expert Repair Knowledge Graph (IERKG) in the industrial sector, addressing the problem of recommending maintenance experts. Specifically, this paper proposes the Knowledge Infused GraphFuseRec (KGFR), which adopts a dual-channel model structure. The KGFR includes the Knowledge Graph Heterogeneous Feature (KGHF) encoder, the Knowledge Graph Isomorphic Feature (KGIF) encoder, and the Knowledge Graph Feature Fusion (KGFF) encoder. Each module collaborates and communicates, dynamically fusing the two types of features in the knowledge graph, achieving more accurate embeddings of experts/users and industrial faults/projects. KGFR enhances the efficiency of industrial fault resolution and reduces the downtime costs incurred during maintenance. We conducted extensive experiments on industrial fault datasets and two public datasets of movies and music. The results demonstrate significant improvements in the performance of our proposed KGFR compared to the state-of-the-art recommendation algorithms.</p>