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Network embedding, aiming to learn low dimensional vectors for nodes while preserving important properties of the network, benefits plenty of network applications. Most existing works focus on network structure, node attribute information or label information. However, many real world networks are often associated with abundant hierarchical labels information, which is potentially valuable in seeking more effective network embedding. Since the information between labels in different levels is hard to correlate or inherit, how to make reasonable use of hierarchical label information to learn more efficient network embedding is still an urgent problem. To address the above issues, a novel hierarchical labels guided attributed network embedding framework (HLANE) is proposed. This framework incorporates hierarchical labels information into network embedding with the help of hierarchical attention layer. HLANE first captures structure and/or attributes information by any existing network embedding method to initialize embeddings. Then the hierarchical attention layer, which builds the connection between the parent labels and the child labels, integrates the hierarchical labels information to guide initial embedding so that it generates hierarchical embedding and entire embedding results with hierarchical labels information. Experiments on real-world datasets demonstrate that the proposed method achieves significantly better performance compared with the state-of-the-art embedding algorithms.