Recently, cyber security events have been gathered as a kind of Cyber Threat Intelligence(CTI) to fight against cyber attacks. Developing a cyber events analysis model to predict the possible threats can assist organizations in providing guidance for decision making. A cyber security event is a complete semantic unit containing all the participating objects (such as attacks assets and organizations) with rich attributes (such as the results and variety of the attack). However, existing cyber security events modeling works ignore the attributes of the objects and analyze the objects' relationships independently. To predict the possible threats for the organizations, we propose a cyber events embedding framework CyEvent2vec to model cyber security events with attributes. First, to effectively depict the cyber security events with attributes that happened in organizations, cyber security events are reconstructed by the organization and processed into the events matrices. Second, to explore the intricate relationships between heterogeneous objects in events, the events matrices are fed into the autoencoder model to get the low-dimensional embeddings. Third, to predict the possible threats for the victim organization, we apply the embeddings to two applications to measure the relevance between the objects: organization threats prediction and threat objects classification. Experiments show CyEvent2vec outperforms the other six representation learning methods on three real-world datasets.