  Educational resource understanding is vital to online learning platforms,
which have demonstrated growing applications recently. However, researchers and
developers always struggle with using existing general natural language
toolkits or domain-specific models. The issue raises a need to develop an
effective and easy-to-use one that benefits AI education-related research and
applications. To bridge this gap, we present a unified, modularized, and
extensive library, EduNLP, focusing on educational resource understanding. In
the library, we decouple the whole workflow to four key modules with consistent
interfaces including data configuration, processing, model implementation, and
model evaluation. We also provide a configurable pipeline to unify the data
usage and model usage in standard ways, where users can customize their own
needs. For the current version, we primarily provide 10 typical models from
four categories, and 5 common downstream-evaluation tasks in the education
domain on 8 subjects for users' usage. The project is released at:
https://github.com/bigdata-ustc/EduNLP.
