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Content-based image retrieval algorithm (CBIR) aims to find semantically matching or similar images with query images. It analyzes visual content in a large number of image databases. It is important to obtain discriminant image representation by feature extraction. With the continuous development of deep learning, the image feature representation method used in image retrieval has gradually changed. The original extraction method is based on manual features. Now it is based on deep features. From the perspective of different feature extraction methods, the recent image retrieval algorithms based on depth feature are reviewed and traced. The image retrieval algorithms based on depth feature are divided into two aspects: depth global feature and depth local feature. The deep convolution feature aggregation technique is emphasized in the deep local feature algorithm. The widely used image retrieval methods of deep global and local feature fusion are summarized. This paper discusses the practical application of deep feature image retrieval technology in remote sensing image retrieval, e-commerce product retrieval and medical image retrieval. And it compares the performance of these feature extraction algorithms in image retrieval accuracy. Finally, the future research trend of depth feature extraction in case image retrieval is forecasted.

With the rapid development of related technologies in the field of natural language processing, as an upstream task of natural language processing, improving the accuracy of named entity recognition is of great significance for subsequent text processing tasks. However, due to the differences between Chinese and English languages, it is difficult to transfer the research results of English named entity recognition into Chinese research effectively. Therefore, the key issues in the current research of Chinese named entity recognition are analyzed from the following four aspects: Firstly, the development of named entity recognition is taken as the main clue, the advantages and disadvantages, common methods and research results of each stage are comprehensively discussed. Secondly, the Chinese text preprocessing methods are summarized from the perspective of sequence annotation, evaluation index, Chinese word segmentation methods and datasets. Then, aiming at the Chinese character and word feature fusion method, the current research is summarized from the perspective of character fusion and word fusion, and the optimization direction of the current Chinese named entity recognition model is discussed. Finally, the practical applications of Chinese named entity recognition in various fields are analyzed. This paper discusses the current research on Chinese named entity recognition, aiming to help researchers understand the research direction and significance of this task more comprehensively, so as to provide a certain reference for proposing new methods and new improvements.

Background The prevalence of age-related cognitive impairment, including dementia, has significantly increased with population aging. It has been shown that cognitive function is associated with gait status. Previously, researchers used statistical analysis methods instead of machine learning methods to study the gait of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) . Objective To develop a model to identify aMCI and AD based on gait status using machine learning methods, explore gait markers differentiating between aMCI and AD, and to assess their possible values as aided tools in diagnosing aMCI and AD. Methods We recruited 102 cases from the Rehabilitation Hospital Affiliated to National Research Center for Rehabilitation Technical Aids, the First People's Hospital of Foshan, and Affiliated Zhongshan Hospital of Dalian University from December 2018 to December 2020, and included 98 of them according to the screening criteria, including 55 patients with aMCI, 10 patients with AD, and 33 healthy controls (HC) . The gait parameters of the participants were collected during performing single-task (free walking) , dual-task (counting backwards in sevens) and another dual-task (counting backwards from 100) using a wearable device. Random forest (RF) algorithm and gradient boosting decision tree (GBDT) algorithm were separately used to establish a model to compare the effect of two algorithms in recognizing three groups, with 10 gait parameters as predictive variables and the physical status (healthy, aMCI, AD) as response variables. Then important features were chosen using a machine learning algorithm combined with recursive feature elimination (RFE) . Results No statistically significant differences were found among the three groups in terms of sex ratio, average age, height, body weight or shoe size (P>0.05) , while the differences in terms of average MMSE score and MoCA score were statistically significant (P