隨著電子商務時代的來臨，部落格已成為現今消費者及企業口碑傳遞的重要工具。而面對網路上大量的部落格湧現，如何提高部落格人氣，已成為全球部落客討論的重點。資訊社會的來臨，網際網路上的結構化資料及非結構化資料日益增長，過去大多數的研究都是透過小樣本進行分析，其資料缺乏樣本多樣性也很難確保預測的準確性。本研究以Medium部落格平台作為研究對象，利用極限梯度提升演算法比較結構化資料與非結構化資料何種資料型態在預測部落格人氣時具有較好的預測能力，最後利用特徵選擇技術找出影響部落格人氣的重要變數、內容關鍵字與關鍵字標籤。
研究結果顯示，利用非結構化資料及非結構化資料所進行預測部落格人氣能使預測能力達到最好。接著，透過特徵選擇本研究發現平均留言次數為影響部落格人氣的重要變數；而部落格的內容中如果能夠跟上現在科技流行的趨勢，可以幫助提升部落格人氣。

With the advent of E-commerce, blog has become an important tool for word-of-mouth communication among consumers and business. With the emergence of a large number of blogs on the Internet, how to improve the blog popularity has become the main topic among bloggers around the world. With the advent of the information society, structured and unstructured data are growing on the Internet. Most of the research were conducted through small samples. The lack of sample diversity and the accuracy of predictions are difficult to ensure. This study uses the Medium blog platform as the research object. Using XGBoost to compare structural data and unstructured data which data types has better predictive accuracy in blog popularity predicting. Finally, using feature selection techniques to identify important variables, content keywords, and keyword tags that affect blog popularity.
The results, predicted blog popularity with unstructured data and unstructured data can ensure that prediction model has best predictive accuracy. Second, we found that average response count is the important variable affecting the blog popularity;If the content of the blog can keep up with the current trend of technology, it can help improve the blog popularity.

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