## 1 Introduction  
Addresses are used to describe a unique spatial location on Earth and are usually expressed in the form of an addressing system [1]. In recent years, with the rapid development of location services, massive amounts of industry data based on addresses as spatial information have started to emerge. Address matching is a crucial application in address services, which compares addresses with the same location in different address databases to obtain the best match with the search address and to determine position on a map [2]. Traditional address matching technology is challenged by the prevalence of high-precision address matching in urban industries, such as logistics and online taxi services. Therefore, an effective address matching method is required to facilitate the provision of accurate and efficient intelligent spatial location services and to promote the development of smart cities. The pattern of arrangement of address elements varies from country to country. For instance, the US address pattern is \"room number + street + state + country\", and it performs well in creating a national geodatabase [3]. Japanese addresses, on the other hand, are coded based on location and geographic relativity, with the overall order being the opposite of the address pattern used in the US, and generally without \"streets\" [4]. Ingeneral, the address patterns of the above countries are nested and relatively standardized. However, Chinese addresses are relatively more difficult to match due to their complex context and rules, mainly due to the following reasons: (1) Chinese addresses are written without separators; (2) Chinese addresses often contain landmarks or POI and topology (e.g., road intersections); (3) different government departments manage addresses, leading to confusion; and (4) address assignment and updating lags behind rapid urban renewal [5]. The data objects of this study are Chinese addresses. Address matching is generally divided into matching based on rule-based or statistical methods and semantic similarity matching based on machine learning and deep learning. Character-based approaches match addresses by calculating their string similarity metrics and then manually establishing a threshold or a particular classifier to identify a match [6; 7]. String similarity metrics include edit distance and its variants [8; 9; 10], Jaccard similarity metric [11], and Jaro distance and its variants [12; 13]. Among them, [PERSON] et al. compared 13 different string similarity metrics for place name matching and found that adjusting the similarity threshold was the key to achieving good performance [14]. In addition, the calculation of cosine similarity between embeddings based on N-grams is also a common method [15; 16]. This method has better performance compared with traditional metrics. Recently, [PERSON] et al. proposed a normalization method based on the Euclidean distance between the address to be processed and the address in the standard library, but it is only applicable to some specific datasets [17]. Another type of methods is address element based, which segments out address elements by rules or statistical methods and then compares the address elements and their hierarchy to determine whether they match [18; 19; 20]. [PERSON] et al. point out that the degree of matching of address elements depends on whether they can be extracted correctly [21]. In general, dictionary queries [22], probability statistics such as CRF [23] and HMM [24], and creating matching rules [25; 26] are the basic ways for retrieving address elements. Another common method is to construct a decision tree consisting of matching rules, each corresponding to a path in the tree. [PERSON] et al. proposed an address matching tree model based on the analysis of the spatial constraint relationship between address elements; this requirement makes the address model more complex [27]. Focusing on the wrong word separation problem, [PERSON] and [PERSON] suggested a method based on a trie tree and finite-state machine [28].