In programming language courses, many online platforms already feature extensive question banks. When teachers prepare course exercises, they need to select several programming problems of equivalent difficulty from the vast existing question bank so that different students can answer different but similar questions. These chosen problems are then assembled into a question pool, from which each student is randomly assigned a programming problem to complete. However, manually selecting problems and forming the pool can be extremely time-consuming due to the sheer volume of available problems. To address this, we propose an Automated Coding Challenge Assembly strategy to help teachers automatically identify similar yet distinct problems from large question banks. By analyzing the similarities in the code submitted by students, our method enables the automatic assembly of programming problems with equivalent difficulty levels. Experimental results have shown that the programming problems selected by this automated method exhibit high relevance and consistent difficulty, providing practical proof of our algorithm’s effectiveness.