Playlists have become the main entry point for users to obtain music resources. This study aimed to investigate the features of playlist titles and covers that attract users and the consequences of playlist selection on music streaming platforms. In this study, 7,606 playlist data were collected from the NetEase Cloud Music platform. The linguistic style of playlist titles was classified based on the language expectancy theory (LET). Artificial intelligence technology was used to recognize and classify the image style of playlist covers according to the content of image features. We then employed hierarchical multiple linear regression and grouping regression to study the moderating effects of playlist features (linguistic style of titles and image style of covers) on the relationship between the number of playlist comments and the number of plays. The findings revealed that (1) the number of playlist comments (comment-count) has a stronger positive relationship with the number of plays (play-count) when the linguistic style of titles, such as concrete, perceptual, interactive, and social languages, appears more frequently and (2) the image style of playlist covers, such as natural, non-natural, painting and text, and portrait images, strengthens the relationships between playlist comment-count and linguistic style of playlist titles on play-count.

播放列表已经成为用户获取音乐资源的主要入口。本研究旨在调查吸引用户的播放列表标题和封面的特征以及在音乐流媒体平台上选择播放列表的后果。在这项研究中，从网易云音乐平台收集了 7,606 个播放列表数据。播放列表标题的语言风格是根据语言期望理论 (LET) 分类的。利用人工智能技术，根据图像特征的内容，对播放列表封面的图像风格进行识别和分类。然后，我们采用分层多元线性回归和分组回归来研究播放列表特征（标题的语言风格和封面的图像风格）对播放列表评论数和播放次数之间关系的调节作用。