<p>Existing emerging topics (ETs) prediction studies primarily focus on characterizing topic-related features and constructing emerging attribute indices. However, the former often overlook the differential influences of academic entities on topics, and the latter rarely incorporate the innovation of knowledge. Therefore, this study proposes an ETs prediction framework that integrates structural and content semantics in dynamic heterogeneous networks to address these limitations. First, we construct dynamic heterogeneous networks comprising four entity types (i.e., papers, authors, topics, and venues) and their relations in real-world scientific systems. Second, a topic-aware mechanism is proposed to enhance topic-related feature characterization. It extracts co-evolutionary features while accounting for the differential influences of academic entities on the topic. Third, to better quantify topics’ emerging degrees, we propose a novel emerging index characterizing two aspects: the popularity score of topic and relative topic novelty. This index captures the innovation of knowledge by projecting content semantics from scientific texts. Last, we predict the emerging degree for each topic using its intrinsic features and co-evolutionary features. Empirical studies on the brain neoplasms and cardiovascular abnormalities datasets confirm the effectiveness of the framework. Overall, this study hopes to enrich the methodological foundations and practical guidance of ETs prediction.</p>