Coding and non-coding RNAs play important roles in various cellular activities, diseases and analysis of new transcriptions. With the development of next-generation sequencing technology, coding and non-coding RNAs can be distinguished quickly and accurately. However, these developed methods cannot analyse small-sized coding and non-coding RNAs very well. Because of this problem, the effect of computational-based approaches in this area has started to increase. In this study, by applying computational-based approaches, the distinction between human-coding and non-coding RNAs was carried out with a deep learning model. The study consists of four stages: obtaining coding and non-coding RNA data, mapping the data with protein mapping techniques, applying the deep learning model, and determining the performance of protein mapping techniques with evaluation criteria. In the study, EIIP, hydrophobicity, integer, CPNR and AVL-based protein mapping techniques were used to convert proteins into numerical expressions. At the end of the study, all protein mapping techniques performed a successful classification process. With the proposed study, it has been observed that the selected protein mapping techniques and deep learning model are effective in the separation of coding and non-coding RNAs.