Hidden collapse columns are the primary cause of potential disasters in coal mines. It is difficult to reliably identify hidden collapse columns only with single layer or inter-layer attributes of conventional three-dimensional seismic, Meanwhile, the three-dimensional features such as the vertical distribution and associated fractures of hidden collapse columns cannot be clearly depicted. To solve these problems, semblance, chaos and other inter-layer (between Ordovician Limestone and upper coal seams) attributes and layer attribute are extracted to forming plane attribute. The anomaly results are recognized and overlaid by Waveform clustering method. significant improving the interpretation accuracy of position in map of the hidden collapse. Furthermore, fault likehood attribute , the structure-oriented filtering processing and co-rendering technology are used to finely describe the shape, fall height and associated fractures of the hidden collapse column. Studies herein show that the comprehensive interpretation technology by using the wave clustering plane attribute and fault likehood attribute can effectively improve the interpretation accuracy and efficiency of the hidden collapse column, Thus it would provide a new way for the identification of the hidden collapse column, and also has broad application prospects.