To solve the specific problem in KBQA, a question answering system is built based on large scale Chinese knowledge base. This system consists of three main steps: recognition of named entity in question, mapping from question to property in KB, and answering selection. Alias dictionary and LSTM language model are used to recognize named entity contained in question, and two different attention mechanisms are combined with bidirectional LSTM for question-property mapping. Finally, exploit results of first two steps are exploited for entity disambiguation and answering selection. The average F1 value of proposed system in NLPCC-ICCPOL 2016 KBQA task is 0.8106, which is competitive with the best result.