Addressing the issue of error propagation in existing research that decomposes the event detection process into two staged tasks of trigger recognition and classification， this paper proposes an event detection method based on type-semantic prompts. This method uses event types as prompt information to guide the model in extracting triggers corresponding to the event types from event text. It enables the parallel execution of trigger recognition and classification tasks， thereby mitigating the issue of error propagation between tasks. Firstly， the cross-attention mechanism is utilized to process the representation of the event text and the prompt template consisting of event types， obtaining a fused prompt representation that integrates the event text information. Then， the cosine similarity between the prompt representation and the event context representation is computed to obtain the probability distribution of the trigger positions corresponding to the event types in the event text. Finally， the position of the trigger corresponding to the event type is determined based on the probability distribution of positions， thus achieving parallel execution of trigger recognition and classification tasks. Experimental results on the ACE2005 and MACCROBAT-EE datasets demonstrate an improvement in the F₁ score of the proposed method in event detection tasks.