The present application relates to artificial intelligence technology. Disclosed is a risk prediction method, comprising: constructing a temporal knowledge graph on the basis of a risk perception factor set that is extracted from a multi-source information set, and performing implicit relationship supplementation and causal relationship supplementation on the temporal knowledge graph, so as to obtain a standard knowledge graph and an event evolutionary graph; performing prediction by using a risk prediction model that is constructed on the basis of a reinforcement learning algorithm, so as to obtain a target risk entity; and performing relationship quantification and degree quantification on the standard knowledge graph, so as to obtain a dependency closeness and an event hazard degree, performing training on the basis of the event evolutionary graph, the dependency closeness and the event hazard degree and in view of a graph neural network and a semi-supervised method, so as to obtain a macro prediction model, and performing prediction by using the macro prediction model, so as to obtain a risk industry corresponding to the target risk entity. In addition, the present application further relates to blockchain technology. The event evolutionary graph can be stored in a node of a blockchain. Also provided in the present application are a risk prediction apparatus, an electronic device and a storage medium. By means of the present application, the accuracy of performing risk prediction on an industry can be improved.