In large-scale and complex software systems, requirement analysis and generation are accomplished through a top-down process, and the construction of tracking relationships between cross-level requirements is very important for project management, development, and evolution. The loosely-coupled contribution approach of open-source systems requires each participant to easily understand the context and state of the requirements, which relies on cross-level requirement tracking. The issue description log is a common way of presenting requirements in open-source systems. It has no fixed template, and its content is diverse (including text, code, and debugging information). Furthermore, the terms can be freely used, and the gap in abstraction level between cross-level requirements is large, which brings great challenges to automatic tracking. In this paper, a correlation feedback method for key feature dimensions is proposed. Through static analysis of the project’s code structure, code-related terms and their correlation strength are extracted, and a code vocabulary base is constructed to alleviate the gap in abstraction level and the inconsistency of terminology between cross-level requirements. By measuring the importance of terms to requirement description and screening key feature dimensions on this basis, the inquiry statement is optimized to effectively reduce the noise of requirement description length, content form, and other aspects. Experiments with two scenarios on three open-source systems suggest that the proposed method outperforms baseline approaches in cross-level requirement tracking and improves F2 value to 29.01%, 7.45%, and 59.21% compared with vector space model (VSM), standard Rocchio, and trace bidirectional encoder representations from transformers (BERT), respectively.