Throwing sports have high technical difficulties and requirements. The traditional learning and training methods require athletes to start from observation and imitation and constantly repeat mechanized training. Athletes have no intuitive comparison and understanding of the overall situation of their own movement technology. Video feedback systems can make up for the shortcomings of traditional methods, so that athletes can more intuitively observe the problems of their own actions. Therefore, this paper puts forward the application research of technical analysis and diagnosis of throwing athletes based on video feedback system, and uses random forest regression algorithm to construct video feedback system. The comparative experimental results show that the students who study and train the movement technology through the video feedback system have higher performance in javelin throwing than the students in the control group, and the performance improvement range is higher. The javelin throwing movement technology is closer to the requirements of the standard movement, which can reduce the wrong movements of javelin throwing. It is more conducive for students to achieve better javelin throwing results.

The purpose is to make defect detection in microelectronic processing technology fast, accurate, reliable, and efficient. A new optical remote sensing-optical beam induced resistance change (ORS-OBIRCH) target recognition and location defect detection method is proposed based on an artificial intelligence algorithm, optical remote sensing (ORS), and optical beam induced resistance change (OBIRCH) location technology using deep convolutional neural network. This method integrates the characteristics of high resolution and rich details of the image obtained by ORS technology and combines the advantages of photosensitive temperature characteristics in OBIRCH positioning technology. It can be adopted to identify, capture, and locate the defects of microdevices in the process of microelectronic processing. Simulation results show that this method can quickly reduce the detection range and locate defects accurately and efficiently. The experimental results reveal that the ORS-OBIRCH target recognition defect location detection method can complete the dynamic synchronization of the IC detection system and obtain high-quality images by changing the laser beam irradiation cycle. Moreover, it can analyze and process the detection results to quickly, accurately, and efficiently locate the defect location. Unlike the traditional detection methods, the success rate of detection has been greatly improved, which is about 95.8%, an increase of nearly 40%; the detection time has been reduced by more than half, from 5.5 days to 1.9 days, and the improvement rate has reached more than 65%. In a word, this method has good practical application value in the field of microelectronic processing.