A method for obtaining user requirements based on NLTK and knowledge graph

The traditional product design scheme can no longer fully meet the current user needs. To solve some problems that live in the period of small and medium batch products' design in traditional solutions, such as long cycles and a lot of differences between design direction and user needs as well as the high technical difficulty of current deep learning-assisted products. This paper proposes a rapid update design method for small batch products based on a knowledge graph, combining data mining and Python module methods. This method aims to reasonably reduce technical requirements, obtain user comments with intelligent crawler plug-ins, complete the extraction of demand information with two key modules of NLTK and Stanford CoreNlp, and realize visualization with a knowledge graph.