Readability is basically concerned with readers' comprehension of given textual materials: the higher the readability of a document, the easier the document can be understood. It may be affected by various factors, such as document length, word difficulty, sentence structure and whether the content of a document meets the prior knowledge of a reader or not. However, simple surface linguistic features cannot always account for these factors in an appropriate manner. To cater for this, we explore in this study a variety of extra features, including syntactic analysis, parts of speech, word embedding, semantic role features and well-written features. The experimental datasets are composed of two parts: one is textbooks of the Chinese language for elementary and junior high schools (K1 to K9) in Taiwan, compiled from three publishers in the academic year of 2009; the other is excellent extracurricular reading materials for students of elementary and junior high schools, collected by the Ministry of Culture in Taiwan. Two readability prediction models, viz. stepwise regression and support vector machine, are evaluated and compared, while the combination of these two models is also investigated so as to further enhance the accuracy of readability prediction. Experimental results reveal that our proposed approach can yield consistently better performance than traditional ones merely with simple surface linguistic features in evaluating text difficulty.