Pornography has existed since the early days of the Internet. With the rapid growth of video-sharing platforms, a new form of pornography, online soft pornography, has emerged. While its harms are evident, few studies have focused on its conceptualization or identification. This paper proposes a deep learning method to identify soft pornography using a new perspective—user engagement. We utilize video data from Bilibili's dance section to measure user engagement from both content creators and users based on interactive characteristics and build a learning model. The results show that the model based on user engagement outperforms the text-based model (87.6% vs. 83.5%) in identifying soft pornography, and the fusion model further improves the performance (88.5%). Principal component analysis reveals that in selective attention-driven participation, users of soft pornography exhibit a significantly higher interest index. This study provides a detailed understanding of user engagement with soft pornography on video platforms and offers new insights for content regulation.