隨著網際網路的快速發展，電子商務變得越來越蓬勃，網站搜尋已成為任何成功的電子商務企業不可或缺的功能。隨著蝦皮購物、Pchome和Amazon等領先企業不斷提高搜尋技術和標準，用戶對電子商務網站的搜尋期望也隨之提高。因此，如何改進搜尋功能，提供更準確、更智能的搜尋結果，成為電子商務企業的一個重要課題。
如果用戶在搜尋時無法找到想要的商品，購物體驗將會受到嚴重影響，甚至導致用戶流失和商機損失。因此，本研究將對用戶的查詢進行命名實體識別（NER）的處理，來理解用戶的意圖，從而讓搜索結果的產品符合用戶的需求。
本研究的目標在於建構一個結合全文搜索及命名實體識別技術的搜尋引擎，並使用Flipkart網站的商品資料集。全文搜索的技術是透過Elasticsearch實現，而NER則從三種模型包含BiLSTM-CRF、BERT-CRF及RoBERTa-CRF中選擇最佳模型，其研究結果顯示RoBERTa-CRF表現最佳，因此使用此模型生成新的資料集作為索引的資料，並用此模型對用戶的查詢進行實體擷取，這些擷取出來的實體將用於建立索引。而在索引之前，將進行本研究的三階段NER過程，透過對用戶查詢的預處理，使索引的結果更準確，進一步提升搜尋精確度和用戶體驗。結果顯示，本研究的方法在搜尋結果上比單獨使用全文搜索的方法表現更佳。

With the rapid development of the internet, e-commerce has become increasingly prosperous, and site search has become an indispensable feature for any successful e-commerce business. As leading companies such as Shopee, PChome, and Amazon continuously improve search technologies and standards, user expectations for e-commerce website searches have also risen. Therefore, improving search functions to provide more accurate and intelligent search results has become a significant challenge for e-commerce companies.
If users cannot find the products they want during a search, their shopping experience will be severely impacted, potentially leading to user attrition and loss of business opportunities. Consequently, this research aims to process user queries through Named Entity Recognition (NER) to understand user intent, thereby aligning search results with user needs.
The objective of this study is to construct a search engine that combines full-text search and NER technologies, using the product dataset from the Flipkart website. The full-text search technology is implemented through Elasticsearch, while the NER is performed using the best model selected from three models: BiLSTM-CRF, BERT-CRF, and RoBERTa-CRF. The research results indicate that RoBERTa-CRF performs the best. Therefore, this model is used to generate a new dataset for indexing, and the same model is employed to extract entities from user queries. These extracted entities are then used to build the index. Before indexing, a three-phase NER process designed in this study will be conducted. Through preprocessing user queries, the accuracy of the indexing results is improved, further enhancing search precision and user experience. The results show that the method proposed in this study performs better in search results compared to using full-text search alone.

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