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The Back-Propagation neural network method is used to identify quark and gluon jets generated by Monte Carlo method. The effects of some factors, such as the architecture of neural network, the input parameters, the training precision and the acceptance cut, on the performance of the neural network are studied in detail. The efficiency and purity of identified quark and gluon jets are calculated for different network architectures. It is found that in order to keep the role of all the input parameters balance, they have to be scaled to the same order of magnitude. Through the study on how the efficiency and purity of the identified quark-and gluon-jets vary with the training precision and acceptance cut, a guidance for how to choose these two parameters is given.

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The dynamical fluctuations inside the quark and antiquark jets are studied using Monte Carlo method. Quark and antiquark jets are identified from the 2-jet events in e + e − collisions at 91.2 GeV by checking them at parton level. It is found that transition point exists inside both of these two kinds of jets. At this point the jets are circular in the transverse plane with respect to the property of dynamical fluctuations. The results are consistent with the fact that the third jet (gluon jet) was historically first discovered in e + e − collisions in the energy region 17-30 GeV.