AbstractThe semiconductor industry occupies a crucial position in the fields of integrated circuits, energy, and communication systems. Effective mass (m_E), which is closely related to electron transition, thermal excitation, and carrier mobility, is a key performance indicator of semiconductor. However, the highly neglected m_E is onerous to measure experimentally, which seriously hinders the evaluation of semiconductor properties and the understanding of the carrier migration mechanisms. Here, a chemically explainable effective mass predictive platform (CEEM) is constructed by deep learning, to identify n‐type and p‐type semiconductors with low m_E. Based on the graph network, a versatile explainable network is innovatively designed that enables CEEM to efficiently predict the m_E of any structure, with the area under the curve of 0.904 for n‐type semiconductors and 0.896 for p‐type semiconductors, and derive the most relevant chemical factors. Using CEEM, the currently largest m_E database is built that contains 126 335 entries and screens out 466 semiconductors with low m_E for transparent conductive materials, photovoltaic materials, and water‐splitting materials. Moreover, a user‐friendly and interactive CEEM web is provided that supports query, prediction, and explanation of m_E. CEEM's high efficiency, accuracy, flexibility, and explainability open up new avenues for the discovery and design of high‐performance semiconductors.