Existing track association models are mostly designed for target association tasks in specific scenarios. When target maneuvering patterns become highly variable or the number of targets fluctuates significantly, these models tend to suffer from degraded association accuracy or extensive missed detections. To address the need for real-time perception of track target attributes in multi-target scenarios, this paper proposes a track association model based on multi-scenario threshold switching. The applicable scenarios and limitations of various track association algorithms are analyzed. Simulation verification is conducted to identify the advantageous performance intervals of different track association algorithms under varying scene densities and noise environments, thereby determining environmental thresholds. During practical operation, the track association model with better performance is selected and switched according to current environmental parameters, enabling more accurate and efficient target attribute classification. Experimental results demonstrate that the proposed threshold-switching track association model achieves a comprehensive Correlation Accuracy Rate(CAR) of 98.16%, maintains an association processing time within 1 s for 50 targets, and keeps the comprehensive Missed Detection Rate(MDR) below 17%.