###### Abstract  
Information in non-standard address texts in Chinese is usually presented with rough content, complex and diverse presentation forms, and inconsistent hierarchical granularity, causing low accuracy in Chinese address parsing. Therefore, we propose a method for parsing non-standard address text in Chinese that integrates the Chinese Toponym Named Entity Recognition (CHTopoNER) model and a dynamic finite state machine (FSM). First, named entity recognition is performed by the CHTopoNER model. Sets of dynamic FSMs are then constructed based on the address hierarchical characteristics to sort and combine the Chinese address elements, thereby achieving address parsing on the Chinese internet. This method showed excellent accuracy in parsing both standard and non-standard placename addresses. In particular, this method performed better in address parsing for disordered or missing hierarchical elements than traditional methods using an FSM. Specifically, this method achieved accuracies of 96.6% and 96.8% for standard and non-standard placenames, respectively. These accuracies increased by 8.0% and 57.1%, respectively, compared with the integrated CHTopoNER model and traditional FSM, and by 7.4% and 19.8%, respectively, compared with the integrated CHTopoNER model and bidirectional FSM. After analysis, the address-parsing method showed good scalability and adaptability, which could be applied to various types of address-parsing tasks. Chinese address parsing; CHTopoNER model; finite state machine; dynamic finite state machine; Chinese internet text +
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0000-0001-9985-3100]M.M. the classification of Chinese address elements considering their characteristics. They also summarized the common vocabulary of spatial relationships in the addresses and the combination patterns among Chinese address elements. [PERSON] et al. [9] built a labeling system for word segmentation of Chinese addresses using Bidirectional Long Short-Term Memory (BiLSTM) to extract contextual features. The optimal label sequence was determined by integrating conditional random fields, thereby improving address entity recognition. In the field of Chinese address parsing, some significant research achievements have been made; nevertheless, several issues remain to be addressed. Specifically, the following challenges persist:  
1. The existing methods for Chinese address element recognition segregate at the character level, without considering traditional Chinese word segmentation techniques commonly used in natural language processing. Consequently, the acquired semantic representations are at the character level and fail to capture the word-level features inherent in the Chinese language. 2. Previous studies on Chinese address element recognition rarely consider the integration of local and global features during feature extraction. Hence, during feature extraction, problems such as missing global or local semantic information arise. 3. Furthermore, after the address parsing process, the traditional finite state machines heavily rely on pre-recorded keywords of address elements or require threshold settings when ordering and combining these elements. While finite state machines (FSMs) and bidirectional FSMs perform well in handling standardized addresses, they struggle to effectively process address information descriptions present in web texts, characterized by hierarchical element disorder and omissions. The innovations introduced in this paper to address the aforementioned issues are summarized as follows:  
1. This study proposes the CHTopoNER model for identifying hierarchical address elements in online text. The innovation of this model is derived from the improved SoftLexicon approach and the integration of BiLSTM and Iterated Dilated Convolutional Neural Network (IDCNN) models to form a Two Channel Neural Network (TCNN) layer.