The incremental topic detection method in news topic detection relies too much on the order of document flow, which makes the clustered topics have drift characteristics. The drift phenomenon of news will affect the recognition of topic detection. This article proposes a Dynamic Subtopic Detection Model for Temporal Text (DSDTT) by designing leader documents for time windows to establish both related and independent connections between topics between windows; The proposal of “Inflection Point Analysis Method” and “CrossMountain” solves the problems of scattered clustering and low discrimination caused by the minimum perplexity determining the number of topics; the model can automatically construct subtopic evolution scenarios based on time windows, effectively alleviating the phenomenon of topic drift. The experiments were conducted in aspects such as the optimal number of topics and topic perplexity, dynamic subtopic detection, and the evolution of subtopic relationships, demonstrating that the Dynamic Subtopic Detection Model for Temporal Text outperforms the perplexity method in selecting the optimal number of topics and exhibits more accurate tracking performance in dynamic subtopic detection.