在全球化競爭和產品複雜程度不斷提升的今日，產品創新設計方法能夠讓企業取得更為持久的競爭優勢。在以消費者為主體導向的競爭市場，產品是否成功的關鍵不能全部託付於設計師，以設計師的自我美感與經驗的累積來開發新產品，很難確保能夠完全滿足消費者的需求。

本研究主要探討產品的造形意象，以汽車造形設計為例，探討汽車側面的造形特徵與意象關聯性。藉由造形特徵的拆解與類神經網路的應用分析，能夠讓設計師確實掌握消費者對於造形的認知，以建立完整的消費者導向之設計模式。

研究前半段為收集汽車側視的樣本圖片，進行造形特徵拆解，接著將定義完的造形特徵進行集群分析，以利後續樣本編碼，配合語意差異法與因素分析後所萃取的意象語彙，將消費者對於造形的意象量化。研究後半段為應用類神經網路來探討造形與意象之間的關聯性，並且導入造形摻合以產生更多新的車形，設計師進而可以快速地掌握消費者對於造形意象的認知。

Recently, global competition and complex level of products are upgrading continuously. With the innovative method of product design, businesses have the advantage of competition permanently. The key of whether product is successful can’t depend on industrial designers in a consumer-driven market. The designers rely their aesthetic feeling and experience to design products but it’s not ensure to satisfy the requirements of consumers.

The purpose of this study is to research in form image of product and takes cars for example. By decomposing the form features and applying Neural Network, the designers can grasp consumer’s desire authentically.

The first half of this study is to collect side view of car pictures and decompose the form features. Then, the Cluster Analysis is used to cluster all form features. By coding all form features and with the adjectives which are chosen by Semantic Differential method and Factor Analysis, we can quantify the relationship between the form features and image. The last half of this study is to research the relationship between the form features and image by applying Neural Network and also applying form blending to make new form of car. Therefore, the designers can grasp consumer’s desire quickly.

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