Recently research of Yan & Huang who are the winners of PASCAL 2010 classification competition indicates that features are the key to the progress in recognition[1, 6]. And actually the description of features is an abstraction of the object needs to learn. Generally speaking, this kind of abstraction can be viewed as an impression[4]. For example, in a recognition task, one is given a picture and asked whether the animal in it is a cat. Usually, people can judge by their impressions of cats. For instance, in someone’s impression, cats have whiskers and are furry with sharp claws. They can see in near darkness and can hear very faint sounds made by mice. In these impressions, some of them are easy to quantify and others are not. Since descriptions of features by computers are different from impressions of humans, computers cannot handle the abstract information if without a suitable model for those features hard to quantify[9]. Deep learning is a tool which can learn features automatically and uses a deep model with internal weights rather than a single value or vector to express those learned features[2, 8]. The structure of deep model can extract abstract features for subsequent classifiers[10]. Moreover, it is worth mentioning that descriptions of features are not same in different levels or different views, which is similar to the idea of granular computing[11]. As a multi-level example, a blind man feels an elephant, only touching some part of it, and concluding what the elephant is like. The man draws an impression on the basis of partial understanding, and the overall judgement is composed of each part of the impression. We obtain various impressions based on one-side viewpoint and each view supplies amount of information which is merged into a concept by a certain structure. In addition, during the understanding of objective things, there is an idea of multilevel processing[5]. For example, when children learn the concept of “cat”, they first notice various kinds of pictures and those pictures supply the pixel information from the lowest level, and then they will use this information to conclude several partial features of cats, such as arms and legs, head, whiskers and eyes. These features are established on the bottom pixels, and they are relatively more abstract and with a higher level. Up above, these features are continued to be combined to a much higher level of features, such as body shape and textures, until to the top decision level to determine whether the animal in the picture is a cat[3]. The idea of multilevel and multi-view is reflected in the deep learning algorithms. Thus, we can develop an abstract deep learning model with multilevel connection layers.