近年來，隨著經濟發展永續議題日漸受到重視。因此，智能化產品服務系統(Smart Product Service System, SPSS)受到了廣泛的關注，通過智能和互連產品處理上下文相關的服務，以提供更貼近客戶的服務。
儘管多數研究透過數據分析模型作為智能PSS的解決方案，但使用單一模型僅能對單一數據類型進行分析，導致提供的服務無法滿足客戶需求。此外，大多數研究提出了一種通用的解決方案，而較少考慮客戶的主觀想法及缺乏與客戶雙向溝通。為了彌補研究的不足，本研究提出一方法，其中包括:(1)透過物件偵測(Object Detection)模型對圖像數據提取客戶偏好。 (2)透過自然語言處理(Natural Language Processing, NLP )方法來分析客戶回饋以優化系統。因此，本研究透過多種深度學習方法分析客戶主觀想法，以獲得更準確的個人需求，並透過分析客戶回饋，以提供更個人化的服務。

In recent years, with the development of the economy, the issue of sustainability has received increasing attention. Therefore, Smart Product Service System (SPSS) has received extensive attention to provide services closer to customers by processing contextually relevant services through smart and interconnected products.
Although most studies use data analysis models as a solution for intelligent PSS, using a single model can only analyze a single data type, resulting in services that cannot meet customer needs. In addition, most studies propose a general solution with less consideration for the subjective thinking of the customer and the lack of two-way communication with the customer. In order to make up for the lack of research, this research proposes a method, which includes: (1) Extract customer preferences from image data through an Object Detection model. (2) Analyze customer feedback through Natural Language Processing (NLP) methods to optimize the system. Therefore, this study analyzes customers' subjective thoughts through various deep learning methods to obtain more accurate personal needs, and analyzes customer feedback to provide more personalized services.

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