Because the traditional single-view methods difficultly obtain better classification performance on different scenes and different forms of multi-view samples, multi-view learning has been widely studied and has become one of the hot topics in recent years. However, in multi-view learning, there may be a special phenomenon of that the difference between samples from the same class of different perspectives is larger than that from different classes of the same perspective, which brings great challenges to multi-view learning, and eventually it will lead to poor multi-view learning. The Parzen window technology is used to construct the public space, and the public space is combined with the original space to obtain the extended space for multi-angle learning, so as to meet the challenges brought by the above special phenomena. Then we use the support vector machine (SVM) to propose a kind of new multi-view learning method, namely a multi-view SVM based on shared hidden space. Experiments on real multi-view data sets verify that the proposed method has good experimental results in response to the above challenges.