At present, patents are divided according to their domains, and cross-domain patent clustering can be realized based on the functional effect, which is of great significance in enterprise innovation design. Accurate extraction of patent functional effect and fast acquisition of optimal clustering results are the key tasks in it. Therefore, a Functional Effect Information-Joint (FEI-Joint) model combining Enhanced Language Representation with Informative Entities (ERNIE) and Convolutional Neural Network (CNN) was proposed to extract the functional effects of patent documents, and the Self-Organizing Map (SOM) algorithm was improved, so as to propose an Early Reject based Class Merge Self-Organizing Map (ERCM-SOM) to realize the patent clustering based on functional effect. FEI-Joint model was compared with Term-Frequency-Inverse-Document-Frequency (TF-IDF), Latent Dirichlet Allocation (LDA) and CNN in the clustering effect after feature extraction, and the results show that the F-measure value of the proposed model was obviously improved than those of other models. Compared with K-Means algorithm and SOM algorithm, ERCM-SOM algorithm has higher F-measure value while has significantly shorter time than that of SOM algorithm. Compared with the patent classification using International Patent Classification (IPC), the clustering method based on functional effect can achieve cross-domain patent clustering effect, which lays a foundation for designers to learn from design methods in other domains.

Patent contains lots of information about background, technology, function and so on, which plays an important role in innovation field. Patent is something created by innovation knowledge, at the same time, it promotes us to make more use of innovation knowledge and break the inherent thinking and the limitation of knowledge, which inspires designers in the process of product design. From the term of innovation design, a new method for extracting innovation knowledge was proposed based on combination feature and maximum entropy classifier. The natural language processing was used, patent terms recognition algorithm was given, and word feature and syntactic feature of the closed package tree in the shortest path were jointed to compute the middle result. After that, the maximum entropy algorithm was applied to extract innovation knowledge based on semantic analysis and mark the attributes of knowledge. The results show that the combination feature can effectively deal with patent issues which need to be solved, and the relationships among the semantic role of knowledge innovation about target function, function principle and position feature in the technical scheme.