In the field of requirement document quality assessment, existing methods mainly focused on textual patterns of requirements. Actually, the cognitive process that experts read and qualitatively measure a requirement document is from the surface to the inside. Inspired by this intuition, this paper proposed a Multimodal Requirement Document Quality Analyzer (MRDQA), a neural model which combines the textual content with the visual rendering of requirement documents for quality assessment. MRDQA can capture implicit quality indicators which are not present in requirement text, such as tables, diagrams, and visual layout. We evaluated MRDQA on the requirement document dataset collected from ZTE and achieved 81.3% accuracy in classifying their quality into three levels (high, medium, and low). We have successful applied MRDQA as a pre-filter in ZTE’s requirement review system. It identifies low and medium quality requirements in advance, thereby allows review experts to focus only on high quality requirements. With this mechanism, we can greatly reduce the workload and speed up the requirement review process.