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The prediction of RNA secondary structure is a fundamental problem in computational biology. However, in the existing RNA secondary structure prediction approaches, none of them explicitly take the local neighboring bases information into account. That is, when predicting whether a base is paired, only the long range correlation is considered. As a substructure consists of multiple bases, it is affected by consecutive bases dependency and their relative positions in the sequence. In this paper we propose a novel RNA secondary structure prediction approach through a combination of Back Propagation (BP) neural network and statistical calculation with Stochastic Context-Free Grammar (SCFG) approach, in which the consecutive bases dependency and their relative positions information in the sequence are incorporated into the predicting process. When performing on tRNA dataset and three species of rRNA datasets, compared to the SCFG approach alone, our experimental results show that the prediction accuracy is all improved. [ABSTRACT FROM AUTHOR]