High-throughput colony sorter is an important equipment for bacteria screening in the biopharmaceutical industry. It uses colony image for intelligent identification and selection, but at present, the equipment only recognizes two-dimensional location information. In order to solve the problem of three-dimensional colony information extraction, this paper proposes a monocular image colony depth extraction algorithm based on transfer learning. The algorithm is based on residual network, combined with multi-scale network structure to extract features, and adopts unsupervised transfer learning training mode, so that the network can estimate the colony depth information. The experimental results show that the average relative error of the algorithm is 0.171, the root mean square error is 6.198, and the log root mean square error is 0.256. The accuracy of the results under the threshold value of 1.25 is increased to 76.4%. The algorithm can obtain the depth information and surface characteristics of the colony at the same time, which provides a referencefor further improving the screening accuracy and effectively selecting the colony.