Backdoor attacks on Deep Neural Networks （DNNs） severely compromise the trustworthiness of model decisions. However， the existing defense methods relying on one-time pruning or global fine-tuning often lead to significant degradation in ACCuracy （ACC）. To address this problem， a Dynamic Targeted Recovery method for backdoor model purification （DTR） was proposed. First， pre-activation was utilized to characterize neuron behaviors， enabling the localization of poisoned neurons with abnormal behaviors. Second， during model purification， targeted recovery was implemented by fine-tuning only the poisoned neurons， thereby effectively avoiding disturbances to clean neurons and maintaining model ACC. Third， in the process of model purification， neuron behaviors were monitored to obtain feedback of the neurons to the purification， thereby realizing dynamic localization of poisoned neurons. Finally， a tabu search strategy was introduced to exclude interference from stubborn neurons with minimal contribution to purification， thereby accelerating the convergence of model purification. Experimental results on 3 benchmark datasets against 6 backdoor attacks including BadNets （Backdoored neural Network） show that the proposed method reduced the average Attack Success Rate （ASR） to at most 0.21% and improved the ACC by 0.10-2.84 percentage points， outperforming 5 other defense methods such as ABL （Anti-Backdoor Learning）. It can be seen that the proposed method effectively overcomes the ACC degradation of traditional methods caused by one-time pruning or global fine-tuning， providing a more reliable method for enhancing DNN security.