Current research on unstructured data processing focuses on technological realization in experiments, while the discussions on the overall architecture and the practical application path in financial investment-research businesses are far from enough. Considering this, this study proposes to develop and design the intelligent investment-research platform through the combination of big data, natural language processing (NLP), and knowledge graph (KG) and implements the platform in real financial investment-research scenarios. With the data collecting, storage, and computation operated through the distributed system Hadoop, the platform integrates traditional text processing technologies and mainstream AI algorithms to form a deep semantic understanding capacity. As a result, the platform is capable of extracting financial text efficiently and storing the information in the form of KGs, and it can also provide intelligent analysis in financial investment research by further exploration and prediction. Taking the tests related to municipal bonds as samples, this research has proved the validity of this platform. The results show that the platform can automatically perform various functions with high precision through the whole process, thus promoting the working efficiency in financial investment research.