[Purpose/significance] Emerging research topics are important carriers for the incubation of scientific and technological frontiers and the diffusion of knowledge. Their early identification provides support for scientific and technological strategic planning and industrial decision-making. This paper integrates multi-source heterogeneous data to construct an integrated framework for early identification and uncertainty measurement, aiming to improve the accuracy and stability of identification, and to provide a quantitative basis for frontier monitoring and strategic assessment through uncertainty analysis. [Method/process] Within the framework of "information perception—cognitive processing—evidence fusion", this paper proposes a multi-source data fusion method based on neural networks and DempsterShafer (DS) evidence theory. First, topics are extracted from multi-source data, a comprehensive emerging-degree indicator is constructed, and features are input into a BP neural network to obtain posterior probabilities. These probabilities are then transformed into basic probability assignments, and multi-source decision-level fusion is achieved through DS evidence theory, thereby identifying emerging research topics. Second, belief intervals are constructed using belief functions and plausibility functions to analyze the evolutionary trends of topic uncertainty. Finally, an empirical study is conducted in the field of stem cells, and development strategies are proposed according to the uncertainty trends of emerging research topics. [Result/conclusion] Compared with single data sources, the multi-source data fusion method can effectively improve the accuracy of identifying emerging research topics and clearly characterize the changing trends of uncertainty in their early stages.