Aiming at the problem that the distance boundaries between word vectors in latent space are not fully considered in discrete word perturbation and embedding perturbation methods， a Semantic Proximity-aware Adversarial Auto-Encoders （SPAAE） method was proposed. Firstly， adversarial auto-encoders were used as the underlying model. Secondly， standard deviation of the probability distribution of noise vectors was obtained on the basis of proximity distance of the word vectors. Finally， by randomly sampling the probability distribution， the perturbation parameters were adjusted dynamically to maximize the blurring of its own semantics without affecting the semantics of other word vectors. Experimental results show that compared with the DAAE （Denoising Adversarial Auto-Encoders） and EPAAE （Embedding Perturbed Adversarial Auto-Encoders） methods， the proposed method has the natural fluency increased by 14.88% and 15.65%， respectively， on Yelp dataset； the proposed method has the Text Style Transfer （TST） accuracy improved by 11.68% and 6.45%， respectively， on Scitail dataset； the proposed method has the BLEU （BiLingual Evaluation Understudy） increased by 28.16% and 26.17%， respectively， on Tenses dataset. It can be seen that SPAAE method provides a more accurate way of perturbing word vectors in theory， and demonstrates its significant advantages in different style transfer tasks on 7 public datasets. Especially in the guidance of online public opinion， the proposed method can be used for style transfer of emotional text.