Objective Employing computational means to explore an efficient deep learning method for improving the prediction accuracy of outer membrane protein topology under the current conditions of small sample size of outer membrane proteins. Method First, selecting and constructing data sets suitable for the prediction of outer membrane protein topology; Second, determining the optimal input of the model through feature screening and comparative experiments; Third, building and optimizing a topology prediction model the TopOMP-capsnet based on capsule network; Finally, evaluation and validation of model performance by comparative congenic methods. Results and conclusions Topology prediction model the TopOMP-capsnet has better performance compared with similar methods, which proves that deep learning technology can identify corresponding sequence patterns under limited sample conditions, and is helpful for large scale classification and screening of outer membrane protein structure and function. Innovation Topology prediction model the TopOMP-capsnet has a three-state prediction accuracy (Q3) of 87.7%, which is superior to traditional machine learning methods.