Increasingly frequent bird activities have brought serious threat on the safe operation of transmission lines, and the existing audio bird-repelling device can not 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 was quantified into different bird response types by combining with the fuzzy theory. Then, an audio bird-repelling experiment was designed, the data of each audio bird-repelling effect is counted, and the initial weight of each audio is obtained, which provided 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 by using the audio weight which is obtained from the experiment, and the contrast experiment with other audio bird-repelling strategies is designed. Experimental data show that the effect of the improved Q-learning algorithm for audio bird-repelling strategy is better than the other audio bird-repelling strategies, which has fast convergence, stable bird-repelling effect, and can reduce the adaptability of birds.