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Automatic evaluation of GOR (Goodness Of pRosody) is a more advanced and challenging task in CALL (Computer Aided Language Learning) system. Apart from traditional prosodic features, we develop a method based on multiple knowledge sources without any prior condition of reading text. After speech recognition, apart from most state-of-the-art features in prosodic analysis, we cultivate more concise and effective feature set from the generation of prosody based on Fujisaki model, and influence of tempo in prosodythe variability of prosodic components based on PVI method. We also propose methods of boosting training without any annotation by mining larger corpus. Results in experiment investigate the GOR score on 1297 speech samples of excellent group of Chinese students aging from 14-16, we can draw several conclusions: On the one hand, adding the knowledge sources from generation and impact of prosody can contribute to 1.76% reduction in EER and 0.036 promotion in correlation than prosodic features alone; On the other hand, final result can be considerably improved by boosting training approach and topic-dependent scheme.