Objective Topic-oriented sarcasm detection aims to judge whether a comment is a sarcastic expression by introducing topics and regarding them as objects. Nevertheless the existing methods based on prompt learning have the problem of relying on manually constructed templates which brings complexity and instability. Methods To tackle this problem a novel design approach was put forward based on template engineering and mapper engineering of prompt learning. A hybrid template integrating continuous and discrete elements interacted with the mapper. By pre-training the template the complexity and instability resulting from manual template selection were mitigated. Subsequently the welldesigned template was concatenated with the original input and fed into the pre-trained model BERT-base-Chinese for classifying the labeled data. On this basis a model named MTPrompt for topic-oriented sarcasm detection was proposed. Results Experiments on the ToScarcasm dataset show that under the scenario of full-batch training data when compared with the existing model TOSPrompt that involves the interaction between manual templates and manual mappers the proposed model MTPrompt achieves significant improvements. Specifically its accuracy was increased by 2. 78% precision by 1. 89% recall by 5. 26% and the F1 score by 3. 54%. Meanwhile under the scenario of small-batch training data both the accuracy and F1 score of this model were higher than those of other baseline models. Conclusion In the application scenario of topic-oriented sarcasm detection the hybrid template method can effectively alleviate the complexity and instability of manually constructed templates and attain better detection performance