Unsupervised intelligent fault diagnosis under different operating conditions is still a challenge task. To obtain high-quality samples and strong model generalization performance, an unsupervised intelligent diagnosis method based on the symmetrical contrast learning framework is proposed for gearbox fault diagnosis. Firstly, a positive sample set and a negative sample set are constructed and enhanced from original signals by adding noise and sequence inversion, which can be fed into two convolutional neural networks (CNN) with the same structure to extract high-dimensional features. Then, a novel symmetrical contrast learning method is proposed to obtain the underline encoding information by measuring the degree of similarity between positive and negative samples. Further, the noisecontrastive estimation loss function is optimized through symmetrical self-supervised learning strategy. In this way, the label information of the sample itself could be effectively used, and the discriminative performance of extracted features from unlabeled samples is improved. Finally, the proposed method is tested and verified on the gearbox data set. Three indicators including clustering accuracy, classification coefficient and partition entropy are constructed for comprehensive evaluation. Experimental results show that the proposed method achieves at least 98% clustering accuracy, which has stronger cluster and generalization ability than other diagnosis approaches.