Increasingly frequent bird activities have brought serious threat on the safe operation of transmission lines,and the existing audio bird-repelling device cannot perennially effectively drive birds due to the lack of intellectuality.In order to solve the above problems,this paper presents an audio bird-repelling strategy based on improved Q-learning algorithm.First of all,in order to evaluate the effect of each audio,the behavior of birds after hearing the audio is quantified into different bird response types by combining with the fuzzy theory.Then,an audio bird-repelling experiment is designed,the data of each audio bird-repelling effect is counted,and the initial weight of each audio is obtained,which provides experimental basis for the audio selection of audio bird-repelling device.In order to make the audio weight more consistent with the actual experimental situation,the weight calculation formula of CRITIC (Criteria Importance Though Intercrieria Correlation) is optimized.Finally,the Q-learning algorithm is improved via the audio weights obtained from the above experiment,and a contrast experiment with other audio bird-repelling strategies is designed.Experimental results show that the improved Q-learning algorithm outperforms other audio bird-repelling strategies,characterized by fast convergence,stable bird-repelling performance,and reducing the adaptability of birds.