Ultrasonic internal inspection is one of the main defect detection methods for the oil and gas pipeline. At present, the location of the defect boundary is inaccurate in the case of small industrial samples for ultrasonic internal inspection. This article proposes a small sample defect recognition method based on multi-dimensional selective search. Firstly, ultrasonic echo features are extracted by two steps, which are feature point extraction based on isolated forest and feature point clustering based on the natural breaks classification method. Secondly, the risk similarity measurement method is proposed. A regression model of waveform characteristics and risk degree is formulated by the boosting tree. Thirdly, multi-dimensional defect similarity is integrated information into a selective search algorithm to realize small sample defect identification. Finally, regional risk metrics such as anomaly scores are used to achieve precise positioning of defect boundaries. Experimental results show that the recall and precision of the small sample defect recognition method based on multidimensional selective search are 95. 08% and 85. 46% , which can effectively solve the problem of inaccurate positioning of the ultrasonic signal defect boundary detection.