Thoracic paravertebral block (TPVB) is a common method of inducing perioperative analgesia in thoracic and abdominal surgery. Identifying anatomical structures in ultrasound images is very important especially for inexperienced anesthesiologists who are unfamiliar with the anatomy. Therefore, our aim was to develop an artificial neural network (ANN) to automatically identify (in real-time) anatomical structures in ultrasound images of TPVB. This study is a retrospective study using ultrasound scans (both video and standard still images) that we acquired. We marked the contours of the paravertebral space (PVS), lung, and bone in the TPVB ultrasound image. Based on the labeled ultrasound images, we used the U-net framework to train and create an ANN that enabled real-time identification of important anatomical structures in ultrasound images. A total of 742 ultrasound images were acquired and labeled in this study. In this ANN, the Intersection over Union (IoU) and Dice similarity coefficient (DSC or Dice coefficient) of the paravertebral space (PVS) were 0.75 and 0.86, respectively, the IoU and DSC of the lung were 0.85 and 0.92, respectively, and the IoU and DSC of the bone were 0.69 and 0.83, respectively. The accuracies of the PVS, lung, and bone were 91.7%, 95.4%, and 74.3%, respectively. For tenfold cross validation, the median interquartile range for PVS IoU and DSC was 0.773 and 0.87, respectively. There was no significant difference in the scores for the PVS, lung, and bone between the two anesthesiologists. We developed an ANN for the real-time automatic identification of thoracic paravertebral anatomy. The performance of the ANN was highly satisfactory. We conclude that AI has good prospects for use in TPVB. Clinical registration number: ChiCTR2200058470 (URL: http://www.chictr.org.cn/showproj.aspx?proj=152839 ; registration date: 2022-04-09).