Accurate Portrait of Big Data of Financial Events Based on Multiple Attention Mechanism

With the rise of knowledge graph technology, the use of entity relationships in big data of financial event to construct accurate portraits of financial events has become an important research direction. By making accurate portraits of big data information on financial events, people can analyze attribute relationships in big data of financial events in detail, fully understand the development trend of financial events, and thus analyze the trends and laws of financial market development. However, there are many research difficulties in financial event big data, such as large text data noise, complex Chinese semantics, and inaccurate extraction of entity relationships, resulting in inaccurate portraits of financial events. In response to the above problems, this paper proposes a financial event big data entity relationship extraction algorithm based on multiple attention mechanism (REMA) to extract entity relationships, and then uses the extracted entity relationship information combined with knowledge graph technology to perform accurate financial event big data portrait. The experimental results show that the precision, recall and F1-score of the entity relationship extraction in the big data of financial events are improved compared with other comparison algorithms without using external resources. Among them, the improvement of precision is 5.6 percentage points, the improvement of recall is 4.6 percentage points, and the improvement of F1-score is 5 percentage points.