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A place name is a textual identification of a specific spatial location by people and is an important carrier of geographical information. The recognition of Chinese place names is of great importance in information retrieval and event extraction. The traditional approach is to transform the recognition of Chinese place names into a sequential annotation problem, with commonly used classification models such as support vector machines and conditional random fields. In this paper, Chinese address recognition is converted into a sequential annotation task, and a multi-feature fusion approach to Chinese address recognition is proposed. A deep learning network architecture model based on the fusion of character, word, and address features is constructed to convert characters, words, and their features into vector representations; finally, the sequential annotation of sentences is performed by CRF to achieve the recognition and extraction of address information. On the autonomously constructed dataset, the proposed method MFBL (Multi-Feature-BiLSTM) improves in accuracy by 4 to 10 percentage points compared to other methods, demonstrating that the MFBL model has better performance in the address recognition task.

Address recognition, as one of the important scenarios of natural language processing in big data applications, is an important and extremely practical technical means. Currently, the evolution of big data applications is being actively promoted, and more and more people are using big data technology to empower power address recognition. Address information in many data assets is the core area of connected devices, and the analysis and mining of core algorithms has extremely high value. This paper first analyzes the application scenarios involved in the common address information of electric power, and formulates the extraction and matching method according to the key points of the core application scenarios and the address recognition requirements; then, based on the data samples, the accuracy and calculation speed of the address recognition method are studied, and the Algorithms are analyzed and compared. Practicality; finally summarize the algorithm, and look forward to ways to improve the algorithm in the future.