The difficulties and methods of unmanned aerial vehicle radar detection technology are studied. It analyzes the model and algorithms of Quantum multi-Pattern Recognition Network（QPRN）. By Grover introducing algorithm optimization theory, Phase Rotation Quantum Multi-Pattern Recognition Algorithm（PRQMPRA） is proposed. The optimization algorithm avoids the defect that both phase rotations are π in the Redundancy Quantum Multi-Pattern Recognition Algorithm（RQMPRA）, which will lead to a decrease in the probability of successful search. Three types of data sets are used to analyze the pattern recognition ability of Error Back Propagation Algorithm（EBPA）, Cross-entropy function-Deep Autoencoder learning Algorithm（CDAA）, RQMPRA and PRQMPRA. In the case of determining the limit error, the results show that PRQMPRA has higher recognition rate and relatively faster operation speed. A multi-pattern recognition algorithm based radar target detection method is proposed to study the target detection problem by pattern classification. Using the above four algorithms for UAV target detection experiments, the results show that PRQMPRA has higher detection accuracy and can maintain a higher discovery probability in the case of low Signal to Noise Ratio（SNR）.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2001-0143