Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Diabetes is a serious threat to health development, because diabetes is a disease that caused most other diseases (complications). Diabetic retinopathy is the most important manifestation of diabetic microangiopathy and is also one of the most common complications in people with diabetes. At present, the diagnosis of diabetic retinal complications mainly depends on the pictures for diagnosis. The fundus images are the main ways to diagnose retinal diseases at present, but the diagnosis process is complicated. Based on this, this paper uses the electronic medical record information of 301 hospitalized patients with diabetes from 2009 to 2013, mainly using the diabetes diagnostic data, diabetes glycosylation data and diabetes biochemical test data, the depth of learning methods and medical diabetes combined with the application of convolution Neural Network Method (CNN) to build a diagnostic model, and thus draw the diagnosis. The main contribution of this study is twofold: 1) In this paper, we apply the CNN method to one-dimensional unrelated data sets and solve the problem of how to do one-dimensional irrelevant data convolution. 2) In this paper, the CNN model is combined with the BN layer to prevent the dispersion of the gradient, speed up the training speed and improve the accuracy of the model. In addition, this model incorporates an adaptive learning rate algorithm and optimizes the model. The experiments show that this method can achieve a training accuracy of 99.85% and a testing accuracy of 97.56%, which is more than 2% higher than that of using logistic regression. The model methods involved in this study can not only be used for the diagnosis of diabetic retinopathy, but also for the diagnosis of other diseases, such as chronic kidney disease, cardiovascular, and cerebrovascular diseases.

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Real-time dense mapping systems have been developed since the birth of consumer RGB-D cameras. Currently, there are two commonly used models in dense mapping systems: truncated signed distance function (TSDF) and surfel. The state-of-the-art dense mapping systems usually work fine with small-sized regions. The generated dense surface may be unsatisfactory around the loop closures when the system tracking drift grows large. In addition, the efficiency of the system with surfel model slows down when the number of the model points in the map becomes large. In this paper, we propose to use two maps in the dense mapping system. The RGB-D images are integrated into a local surfel map. The old surfels that reconstructed in former times and far away from the camera frustum are moved from the local map to the global map. The updated surfels in the local map when every frame arrives are kept bounded. Therefore, in our system, the scene that can be reconstructed is very large, and the frame rate of our system remains high. We detect loop closures and optimize the pose graph to distribute system tracking drift. The positions and normals of the surfels in the map are also corrected using an embedded deformation graph so that they are consistent with the updated poses. In order to deal with large surface deformations, we propose a new method for constructing constraints with system trajectories and loop closure keyframes. The proposed new method stabilizes large-scale surface deformation. Experimental results show that our novel system behaves better than the prior state-of-the-art dense mapping systems.