The　purpose　of　graph　embedding　is　to　encode　the　known　node　features　and　topological　information　of　graph　into　low-dimensional　embeddings　for　further　downstream　learning　tasks.　Graph　autoencoders　can　aggregate　graph　topology　and　node　features,　but　it　is　highly　dependent　on　the　gradient　descent　optimizer　with　a　large　iterative　learning　time,　and　susceptible　to　local　optimal　solutions.　Thus,　we　propose　Graph　Convolutional　Extreme　Learning　Machine　Autoencoder.　To　address　the　limitation　that　the　extreme　learning　machine　autoencoder　cannot　use　topological　information,　the　graph　convolution　operation　is　introduced　between　the　input　layer　and　the　hidden　layer　to　improve　the　representation　ability　of　the　graph　embedding　obtained.　Experiments　of　link　prediction　and　node　classification　on　5　real　datasets　show　that　our　method　is　effective.　©　2023　IEEE.