In the era of big data, intellectual-property-oriented scientific and technological resources show trends such as large data scale, high timeliness, and low value density, which poses severe challenges for the effective use of intellectual property resources. At the same time, the demand for the mining of hidden information in intellectual property rights is increasing in various countries, making the construction of intellectual-property-oriented scientific and technological resource portraits a current research hotspot. This study aim at building a portrait of intellectual property through intelligent data acquisition, entity recognition and visualization. However, the existing methods for constructing scientific and technological resource portraits are only suitable for structured data and ignore the impact of words’ part of speech on the semantic understanding of sentences. Therefore, a novel algorithm is proposed for the construction of intellectual-property-oriented portraits of scientific and technological resources. Regarding the automatically acquired intellectual property resources, attention mechanism of part-of-speech level is introduced to improve the accuracy of entity recognition, and intellectual-property-oriented scientific and technological resource portraits are visually constructed. Compared with the existing methods, the proposed method has the following advantages: 1) This utilizes the part-of-speech information of words to learn the semantic meaning of sentences, and integrates the attention mechanism to avoid ambiguities in semantic understanding in a supervised way. 2) This model can intelligently and automatically complete sci-tech data acquisition, named entity recognition, and construction of scientific and technological resource portraits. 3) Extensive experiments demonstrate that our method performs better than baselines in named entity recognition by utilizing the part of speech of words for supervised learning.