The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on a <italic>single</italic> domain (e.g., politics). However, correlations exist commonly across multiple news domains, and thus it is promising to simultaneously detect fake news of <italic>multiple</italic> domains. Based on our analysis, we pose two challenges in multi-domain fake news detection: 1) <italic>domain shift</italic>, caused by the discrepancy among domains in terms of words, emotions, styles, etc. 2) <italic>domain labeling incompleteness</italic>, stemming from the real-world categorization that only outputs one single domain label, regardless of topic diversity of a news piece. In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="zhu-ieq1-3185151.gif"/></alternatives></inline-formula> FEND) to address these two challenges. We model news pieces from a multi-view perspective, including semantics, emotion, and style. Specifically, we propose a Domain Memory Bank to enrich domain information which could discover potential domain labels based on seen news pieces and model domain characteristics. Then, with enriched domain information as input, a Domain Adapter could adaptively aggregate discriminative information from multiple views for news in various domains. Extensive offline experiments on English and Chinese datasets demonstrate the effectiveness of M<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="zhu-ieq2-3185151.gif"/></alternatives></inline-formula> FEND, and online tests verify its superiority in practice. Our code is available at <uri>https://github.com/ICTMCG/M3FEND</uri>.