With the free supervised signals/labels created by pretext tasks, self-supervised learning (SSL) can learn effective representation from unlabeled data, which has been verified in various downstream tasks. Existing pretext tasks usually first perform explicit linear or nonlinear transformations on the original view data, thus forming multiple augmented view data, then learn the representation by predicting the corresponding transformations or maximizing the consistency among the above views. It is found that such self-supervised augmentations (i.e., the augmentations of the data itself and self-supervised labels) benefit the learning of not only the unsupervised pretext tasks but also the supervised classification task. Nevertheless, few work focus on this at present, while existing works either take the pretexts as the auxiliary of downstream classification task and adopt the multi-task learning or jointly model the downstream task labels and self-supervised labels in a multi-label learning way. Actually, there are inherent differences between downstream and pretext tasks (e.g., semantic, task difficulty, etc.), which inevitably result in the competitions between them and bring risks to the learning of downstream tasks. To challenge this issue, this study proposes a simple yet effective SSL multi-view learning framework (SSL-MV), which avoids the learning interference of self-supervised labels on downstream labels through performing the same learning as downstream tasks on the augmented data views. More interestingly, with the multi-view learning, the proposed framework naturally owns the integration inference ability, which significantly improves the performance of downstream supervised classification tasks. Extensive experiments on benchmark datasets demonstrate the effectiveness of SSL-MV.