近年來，動漫(動畫、漫畫與線上遊戲)越來越受歡迎。全球資訊網也陸續出現收集大量包括故事情節、動漫角色、作者等動漫相關資訊的動漫網站。多數動漫網站都提供使用者文字檢索的功能，以搜尋動漫網站文字內容。但是動站網站若能提供根據文字與圖形風格來搜尋圖文內容，對於動漫使用者而言，將更為方便。圖文風格可能是漫畫人物的繪畫風格、動畫故事的敘事風格等等。 為了方便使用者以圖文風格進行搜尋相關資訊，本論文根據動漫關連探勘技術，研究並開發一個動漫網站的搜尋引擎：動漫魔鏡，以提供使用者根據圖文關連來搜尋動漫網站中風格相似的動漫資訊。本論文的搜索方法關聯了圖像特徵和文字特徵。首先，針對圖像特徵，由於動漫角色是動漫的靈魂，因此經過動漫臉部偵測後，我們以電腦視覺中的局部二值樣式(Local Binary Pattern, LBP)與灰階值分佈來抽取並表示動漫角色的臉部特徵。針對文字特徵，我們利用一般全文檢索技術來擷取文字特徵。接著，運用階層式分群技術將文字特徵與圖像特徵值轉換為文字與圖像關鍵詞。最後，以語意主題模型中的隱含狄利克雷分布(Latent Dirichlet Allocation, LDA)分析圖文關鍵詞的潛在語意，並據此計算動漫網頁之間的相似性。實驗結果顯示本論文所研發的風格搜尋，其效果優於其他三種基本作法。 / Animations, comics, and games (ACG) have become more and more popular in recent years. There exist many ACG web sites which contain lots of textual and visual information on stories, characters and authors of animations, comics and games. Most ACG web sites provide users text retrieval capability to search for textual contents. However, there is a need for users to search for textual and visual contents by styles. Examples of styles are drawing styles of comic characters, narrative styles of animation stories and so on. In order to help users to search for textual and visual contents by similar styles, this thesis investigates and develops a search engine, Comirror, for ACG web sites based on latent correlation between textual and visual contents. First, while facial styles of characters play important roles in ACG, after comic face detection, Local Binary Pattern (LBP) along with gray-value histogram is utilized to extract and represent the visual features. For the textual contents, traditional full-text indexing technique is employed to extract textual features. Then, hierarchical clustering is performed to quantize and transform the textual and visual features into textual and visual words. Finally, Latent Dirichlet Allocation (LDA) is utilized to discover the latent semantic correlation between visual and textual words. Experiments show that the developed approach performs better than the other baseline approaches.