Aiming at the problem that the existing methods in the big data environment cannot extract the emotional features of microblog sufficiently and the average accuracy of analysis results is low, a microblog emotion analysis method using deep learning in spark big data environment is proposed. First, the Jieba word segmentation method is used to process text comments, so as to reduce the interference of irregular grammar and nonstandard words on the emotion analysis task of microblog text. Then, features based on affective rules, unary word features, syntactic features, and dependent word collocation features are selected. In order to prevent the dimension disaster caused by excessive feature dimensions, the feature selection method of information gain is used to reduce the dimension of features. Finally, a microblog emotion analysis method based on deep belief network (DBN) is established, and the DBN is parallelized through spark cluster to shorten the training time. Experiments show that when the feature set is composed of TOP2000 features, the classification accuracy of the fusion of four features is 90.94%, which is higher than that of the comparison method. In addition, the training time of DBN algorithm parallelized by spark cluster is only 27.78% of that of single machine. Therefore, compared with the comparison method, the proposed method can significantly improve the performance of the microblog emotion analysis system.

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With image analysis as the core for multitarget detection and intelligent tracking, mostly applying the Faster R-CNN or YOLO framework, the MOTA score for multitarget tracking is low in the face of complex working environments. Therefore, further research into computer vision techniques is carried out to design new multitarget detection and intelligent tracking methods. Based on the small-aperture imaging model, the principle of lens distortion was analyzed, and a camera calibration and image calibration scheme was designed to obtain effective environmental images. The attention mechanism is introduced to optimise the structure of deep learning networks, and a computer vision detection algorithm based on this is applied to complete regional multitarget detection. The distance between each target and the body is then measured in combination with binocular vision principles. Finally, the spatiotemporal context algorithm is applied to perform simulation calculations to obtain the multitarget intelligent tracking results. The experimental results show that the mean MOTA score of the proposed technique is 0.87 in the night environment, which is 24.14% and 28.374% better than the neural network-based and machine vision-based tracking methods, respectively; in the daytime environment, the mean MOTA score of the multitarget tracking results of the technique is 0.94, which is 28.72%, and the mean MOTA score of 0.94 for the multitarget tracking results in the daytime environment was 28.72% and 22.34% higher than the other two methods.