Effective patent recommendation plays a crucial role in bridging the gap between enterprise technological demands and patent supply. However, semantic mismatches and incomplete demand expressions often hinder accurate supply–demand matching. This research proposes a demand-driven patent recommendation method. First, content analysis and topic clustering were used to construct an enterprise demand element system, dividing the demand content into five elements: materials, methods, efficacy, products, and applications. Based on the completeness of these elements, enterprise demands were further classified into explicit and implicit types. Second, an enterprise technical problem space and a patent solution space were established, identifying ten types of enterprise technical problems and fifteen types of patent solution categories. These were connected through supply–demand elements to build corresponding correlation systems for explicit and implicit demands. Finally, according to different types of supply–demand correlations and demand characteristics, differentiated patent recommendation methods were designed. Taking various demands in the lithium battery industry as empirical cases, the results show that the proposed method based on demand classification and supply–demand element association effectively achieves accurate patent matching and addresses the challenges caused by incomplete demand information. The study provides an intelligent, content-based recommendation pathway for enterprise technology acquisition and patent transformation, offering theoretical and practical significance for enhancing patent commercialization and improving the efficiency of technological achievement transformation.

Patents are essential carriers of technological innovation, and their efficient transfer is critical for accelerating technological iteration in the lithium battery industry and supporting sustainability in the new energy sector. However, existing patent recommendation methods lack frameworks for handling heterogeneous enterprise demands, which limits the accuracy of supply–demand matching. This study proposes a knowledge graph-based differentiated patent recommendation framework for enterprise technological demands in the lithium battery domain. A five-element content framework—material, method, efficacy, product, and application—is constructed from both the supply and demand sides. Enterprise demands are classified into complete and incomplete types based on element coverage, and patent supply knowledge graphs are built for potentially relevant patents. Two differentiated recommendation methods are then developed. For complete demands, the Precision Recommendation Method for Complete Technological Demands integrates BERT-based semantic encoding, TransE-based structural modeling, and RAG-based constraint retrieval to achieve precise matching under full element coverage. For incomplete demands, the Fuzzy Recommendation Method for Incomplete Technological Demands incorporates multi-source enterprise data to enrich demand categories and constructs augmented query contexts to generate diversified candidate patent sets. Empirical validation based on 25 demand-driven patent transfer cases shows that the PR-CTD method exactly identifies the actual transferred patents in three cases. The FR-ITD method ranks 6 out of 14 actual transferred patents within the Top-5 results, while the remaining cases are all within the Top 13. These results demonstrate the effectiveness of the proposed framework in real-world patent transfer scenarios. This study provides a novel theoretical perspective for the structured modeling of heterogeneous technological demands and supply–demand semantic matching. It also offers practical value by improving the efficiency of patent retrieval and matching, thereby supporting patent technology transfer in the lithium battery industry.

A large number of potential knowledge associations in scientific and technological innovation activities have not yet become explicit. How to identify potential valuable innovation opportunity clues from complex knowledge structures has therefore become an important issue in intelligence analysis research. This study takes the field of rice drought-tolerant breeding as an empirical case. Based on PMC full-text literature data, the LightRAG model was employed to extract innovation-related entities and their semantic relationships, including varieties, genes, proteins, phenotypes, and technological methods. A technology-data heterogeneous network for rice drought-tolerant breeding was then constructed, and the HetGNN link prediction method was introduced to predict potential relationships within the network. To evaluate the effectiveness of the proposed model, literature published from 2006 to 2020 was used to construct the training network, while newly emerging relationships extracted from literature published between 2021 and 2023 were used as a future validation set. Adamic-Adar and Node2Vec were further selected as baseline models for comparison. The experimental results show that the proposed method achieved an AUC of 0.8901, an AP of 0.9190, and an F1@0.5 of 0.8322 on the internal testing set, outperforming the baseline models in overall performance. In the temporal holdout validation, the model was able to identify some newly emerging knowledge associations that subsequently appeared in the 2021–2023 literature. The prediction results based on the full dataset indicate that the potential relationships are mainly concentrated in influence relationships between data elements and drought-tolerant phenotypes, as well as support relationships between technological methods and drought-tolerant phenotype research. This study constructs an analytical framework consisting of “innovation element extraction, heterogeneous network modeling, temporal holdout validation, and potential relationship interpretation,” thereby providing a methodological reference for identifying potential innovation opportunity clues from complex scientific knowledge structures.