To address the challenge of identifying unseen categories in zero-shot Human-Object Interaction （HOI） detection， a zero-shot HOI detection method via multimodal collaborative prompt optimization， named MCPNet （Multimodal Cooperative Prompt Network）， was proposed to enhance the model's generalization ability under zero-shot conditions. The method leverages text-driven prompt learning and visual feature generation mechanisms to improve the recognition performance of unseen interaction categories and the overall generalization performance from both semantic and visual modeling perspectives. First， in terms of semantic modeling， a prompt-guided module was developed by integrating human-designed prompt templates with learnable prompts， which were encoded by the Contrastive Language-Image Pre-training（CLIP）text encoder to produce fine-grained semantic representations. Then， a generation module conditioned on learnable prompts was introduced， in which a Variational AutoEncoder （VAE） was employed to model the latent semantic space and to synthesize discriminative image features for unseen categories， thus alleviating the long-tailed distribution problem. Finally， for visual modeling， a collaborative mechanism between local and global features was proposed. Local interaction region features were integrated with global image semantics as inputs to the interaction detection head， enhancing the recognition performance of HOIs in complex scenes. Experimental results in zero-shot detection tasks on the HICO-DET （Humans Interacting with Common Objects DETection） dataset show that the proposed method achieves significant gains in mean Average Precision （mAP） of unseen categories， surpassing CLIP4HOI （towards adapting CLIP for practical zero-shot HOI detection） method by 8.59% under the Unseen Combination （UC） setting， by 9.94% under the Rare-First UC （RF-UC） setting， by 8.24% under the Non-rare-First UC （NF-UC） setting， and by 9.37% under the Unseen Object （UO） setting. The conclusion verifies the effectiveness of the proposed method.