Understanding and modeling time remains a key challenge in today's video understanding systems. With language playing a central role in driving powerful generalization, foundational video-language models must inherently capture a sense of temporality. However, Video-text retrieval faces three challenges: Parameter efficiency bottlenecks (traditional prompt learning requires adjusting over 10 % of parameters, leading to a sharp increase in computational costs), limitations of static course strategies (manually defined difficulty thresholds cause pseudo-label biases in highly abstract actions), and insufficient adaptability to heterogeneous architectures (cross-modal hybrid expert frameworks rely on shared weights, restricting the flexible combination of heterogeneous models such as CLIP and ViT). To address these challenges, this paper proposes the Dynamic Curriculum Learning with Adaptive Prompt Mixture of Experts (DynaCLIP) framework. Through the collaborative optimization of the Adaptive Prompt Mixture of Experts (APMoE) module and dynamic curriculum learning, the parameter efficiency and dynamic adaptability are significantly improved. APMoE adopts orthogonal initialization expert pools and cross-modal routing networks. It freezes the pre-trained backbone networks (such as CLIP/BERT) on the premise of fine-tuning only less than 1 % of parameters (static expert pool + dynamic prompt generator), and generates instance-aware text encoder prompts by dynamically selecting Top-k experts and weighted fusion. Here, these instanceaware prompts are implemented as continuous soft prompts in the embedding space, rather than discrete natural language text. Meanwhile, a two-dimensional difficulty quantification system is constructed based on the verb abstraction level (VerbNet three-level classification) and BERT semantic similarity. Combined with the improved binary search strategy to balance high-confidence samples and diversity exploration, a cognitiveinspired course learning mechanism is formed. Experiments show that DynaCLIP achieves 7.4 % R@1 on the TEMPO dataset, 67.5 % accuracy in temporal inference. The code is available at: https://github.com/18162195164/DynaCLIP.