This paper addresses continuous affect modeling in SemEval-2026 Task 2 through two task-specific architectures tailored to static state estimation and dynamic change prediction. To mitigate semantic ambiguity and annotation subjectivity in Subtask 1, a hard-prompt-based regression model is developed and enhanced with unsupervised contrastive learning (SimCSE) and supervised contrastive calibration (SCL) grounded in an external affect lexicon. This design improves the structural consistency and scale stability of textual representations in the Valence–Arousal (V/A) space. For Subtask 2a, which involves irregular time intervals and historical dependencies, a Time-Aware LSTM architecture is introduced to integrate current affective states with temporally enriched historical trajectories. Experimental results show that the YNU-HPCC system ranks 2nd in both subtasks. In Subtask 1, the Valence and Arousal scores are 0.677 and 0.528, respectively; in Subtask 2a, they are 0.692 and 0.647.