The transformation of scientific and technological innovations into practical applications through patent recommendation is of great significance for realizing the economic value of science and technology and promoting socio-economic development. However, existing patent recommendation algorithms often overlook the multimodal information embedded in patents, leading to recommendation results that fail to comprehensively reflect the value and application potential of patents. Consequently, this affects the accuracy of matching patents with the needs of companies. To address this issue, this study proposes a novel patent recommendation algorithm based on a multimodal heterogeneous graph network (MHGN). The proposed method first utilizes pre-trained models to initialize the representation of multimodal information, including the textual and image attributes of patents as well as company information. Then, a graph attention network is employed to learn the preference representations of companies across different modalities. Based on this, the relationship weights of company-patent interactions are further learned based on the similarity of preference representations, and a graph convolutional network is designed to learn the node preference representations of companies and patents. Finally, to better integrate the multimodal information, an adaptation vector is introduced and an attention mechanism is used to fuse the node preference representations with multimodal representations. In addition, four real-world patent datasets from university-to-company transfers are constructed, and experiments are conducted comparing the proposed model with seven advanced baseline models. The results demonstrate that the proposed model significantly outperforms the baselines across all evaluation metrics. Both the datasets and the source code of the proposed model are released, providing robust data and model support for future research in patent recommendation and the transformation of scientific innovations.