Aiming at the problem of limited classification accuracy due to insufficient mining of regular texture features in band selection of hyperspectral remote sensing images of crop planting areas， a band selection algorithm based on Mahalanobis distance and Gibbs-Markov Random Field （GMRF） spatial filtering was proposed. Firstly， for the regular texture features commonly found in crop planting areas， spatial filtering of hyperspectral images was performed by establishing a GMRF model， which retained and strengthened the texture features while reducing noise and redundant information， and enhanced the differences between ground object features. Then， a category separability metric was established on the basis of Mahalanobis distance combined with the ratio method， the contribution value of each band to the metric was calculated， and the bands were ranked according to the contribution values， thereby the specified number of top-ranked bands were selected as the output of the algorithm. The Indian Pines hyperspectral dataset， which contains a large number of crop planting areas， was used for band selection and maximum likelihood classification experiments， and the results show that compared with the optimal performance indexes of the three reference algorithms： genetic algorithm， successive projections algorithm， and density peak clustering algorithm， the proposed algorithm’s average correlation， overall classification accuracy and Kappa coefficient were improved by 3.37%， 2.90% and 6.70%， respectively. It can be seen that the proposed algorithm integrates crop spatial texture and spectral covariance features effectively， providing a feature selection scheme with clear physical interpretation for crop classification and growth monitoring in precision agriculture.

To address the problems that many existing studies ignore the correlation between interlocutors’ emotions and sentiments， a sentiment boosting model for emotion recognition in conversation text was proposed， namely Sentiment Boosting Graph Neural network （SBGN）. Firstly， themes and dialogue intent were integrated into the text， and the reconstructed text features were extracted by fine-tuning the pre-trained language model. Secondly， a symmetric learning structure for emotion analysis was given， with the reconstructed features fed into a Graph Neural Network （GNN） emotion analysis model and a Bi-directional Long Short-Term Memory （Bi-LSTM） sentiment classification model. Finally， by fusing emotion analysis and sentiment classification models， a new loss function was constructed with sentiment classification loss function as a penalty， and the optimal penalty factor was adjusted and obtained by learning. Experimental results on public dataset DailyDialog show that SBGN model improves 16.62 percentage points compared with Dialogue Graph Convolutional Network （DialogueGCN） model， and improves 14.81 percentage points compared with the state-of-art model Directed Acyclic Graph-Emotion Recognition from Conversation （DAG-ERC） in micro-average F1. It can be seen that SBGN model can effectively improve the performance of emotion analysis in dialogue system.

Most of the existing drowsiness recognition algorithms are based on machine learning or deep learning， without considering the relationship between the sequence of human eye closed state and drowsiness. In order to solve the problem， a drowsiness recognition algorithm based on human eye state was proposed. Firstly， a human eye segmentation and area calculation model was proposed. Based on 68 feature points of the face， the eye area was segmented according to the extremely large polygon formed by the feature points of human eye， and the total number of eye pixels was used to represent the size of the eye area. Secondly， the area of the human eye in the maximum state was calculated， and the key frame selection algorithm was used to select 4 frames representing the eye opening state the most， and the eye opening threshold was calculated based on the areas of human eye in these 4 frames and in the maximum state. Therefore， the eye closure degree score model was constructed to determine the closed state of the human eye. Finally， according the eye closure degree score sequence of the input video， a drowsiness recognition model was constructed based on continuous multi-frame sequence analysis. The drowsiness state recognition was conducted on the two commonly used international datasets such as Yawning Detection Dataset （YawDD） and NTHU-DDD dataset.Experimental results show that， the recognition accuracy of the proposed algorithm is more than 80% on the two datasets， especially on the YawDD， the proposed algorithm has the recognition accuracy above 94%. The proposed algorithm can be applied to driver status detection during driving， learner status analysis in class and so on.

A new image retrieval method based on enhanced micro-structure and context-sensitive similarity was proposed to overcome the shortcoming of high dimension of combined image feature and intangible combined weights. A new local pattern map was firstly used to create filter map, and then enhanced micro-structure descriptor was extracted based on color co-occurrence relationship. The descriptor combined several features with the same dimension as single color feature. Based on the extracted descriptor, normal distance between image pairs was calculated and sorted. Combined with the iterative context-sensitive similarity, the initial sorted image series were re-ranked. With setting the value of iteration times as 50 and considering the top 24 images in the retrieved image set, the comparative experiments with Multi-Texton Histogram (MTH) and Micro-Structure Descriptor (MSD) show that the retrieval precisions of the proposed algorithm respectively are increased by 13.14% and 7.09% on Corel-5000 image set and increased by 11.03% and 6.8% on Corel-10000 image set. By combining several features and using context information while keeping dimension unchanged, the new method can enhance the precision effectively.