Information extraction is one of the most important tasks in natural language processing. A hierarchical Lexical-Semantic Pattern (LSP) method for the extraction of financial events was proposed for the problem of information extraction in natural language processing due to linguistic diversity, ambiguity and structure. Firstly, a financial event representation model was defined. Secondly, a word vector method based on deep learning was used to realize the automatic generation of synonymous concept dictionary. Finally, some hierarchical LSPs based on finite state machine were used to extract various kinds of financial events. The experimental results show that by using the proposed method various kinds of financial events can be accurately extracted from the financial news text, and for 26 types of financial events recognition the micro average precision is 93.9%, the micro average recall is 86.9%, the micro average F1 value reaches 90.3%.