Planetary　gearboxes　have　various　merits　in　mechanical　transmission,　but　their　complex　structure　and　intricate　operation　modes　bring　large　challenges　in　terms　of　fault　diagnosis.　Deep　learning　has　attracted　increasing　attention　in　intelligent　fault　diagnosis　and　has　been　successfully　adopted　for　planetary　gearbox　fault　diagnosis,　avoiding　the　difficulty　in　manually　analyzing　complex　fault　features　with　signal　processing　methods.　This　paper　presents　a　comprehensive　review　of　deep　learning-based　planetary　gearbox　health　state　recognition.　First,　the　challenges　caused　by　the　complex　vibration　characteristics　of　planetary　gearboxes　in　fault　diagnosis　are　analyzed.　Second,　according　to　the　popularity　of　deep　learning　in　planetary　gearbox　fault　diagnosis,　we　briefly　introduce　six　mainstream　algorithms,　i.e.　autoencoder,　deep　Boltzmann　machine,　convolutional　neural　network,　transformer,　generative　adversarial　network,　and　graph　neural　network,　and　some　variants　of　them.　Then,　the　applications　of　these　methods　to　planetary　gearbox　fault　diagnosis　are　reviewed.　Finally,　the　research　prospects　and　challenges　in　this　research　are　discussed.　According　to　the　challenges,　a　dataset　is　introduced　in　this　paper　to　facilitate　future　investigations.　We　expect　that　this　paper　can　provide　new　graduate　students,　institutions　and　companies　with　a　preliminary　understanding　of　methods　used　in　this　field.　The　dataset　can　be　downloaded　from　https://github.com/Liudd-BJUT/WT-planetary-gearbox-dataset.