In the context of global aging, the health problems of the elderly have gradually become prominent, and cognitive stimulation dialogue is an important means to maintain the cognitive health of the elderly. Previous researchers constructed a Chinese cognitive stimulation dialogue dataset (CSConv) that includes emotional support, thereby initiating research in the field of Chinese cognitive stimulation dialogue. However, the authors did not fully model the logical reasoning relationships within cognitive stimulation dialogues and did not effectively utilize the guiding role of strategy labels during dialogue generation. This study regards cognitive stimulation dialogue generation as a multi-task integration thinking and reasoning process, and models the logical relationship among emotion classification tasks, decision-making tasks and dialogue response generation tasks as a reasoning process to guide the generation of large language models. For decision-making tasks, this paper proposes a decision-making model with a hierarchical encoder structure. The results of the decision-making experiment show that the decision-making model improves the accuracy of the cognitive stimulation therapy principles and emotional support strategies decision-making tasks by 3.96% and 2.1%, respectively. For multi-task logical thinking and reasoning process, this paper proposes a multi-task integration method to combine the models corresponding to the three tasks. The experimental results show that the multi-task integration method has improved BLEU-4 by 7.95% compared with the previous baseline, indicating that the dialogue response ability has been improved, proving the effectiveness and advancement of this method.