Automotive system functionalities spread over a wide range of sub-domains ranging from non-driving related components to complex autonomous driving related components. The requirements to design and develop these components span across software, hardware, firmware, etc. elements. The successful development of these components to achieve the needs from the stockholders requires accurate understanding and traceability of the requirements of these component systems. The high-level customer requirements transformation into low level granularity requires an efficient requirement engineer. The manual understanding of the customer requirements from the requirement documents are influenced by the context and the knowledge gap of the requirement engineer in understanding and transforming the requirements. The manual way of understanding the requirements of the automotive systems always involve a certain amount of ambiguity, misunderstanding, bias etc. in analyzing the functionality of the requirements. The complex automotive system, which is solely developed based on human understanding always causes some violations in transforming the actual requirements from the stockholders in a product functionality. Hence, to mitigate this human influence on this aspect of requirement understanding, an intelligent system, which either to assists the manual requirement analysis or completely analyze the requirements alone is required. In this regard, an intelligent system is proposed here to analyze the automotive requirements efficiently by reducing human conflict, manual efforts, and to improve design and execution performance of an automotive component. The proposed system uses deep learning based Natural Language Processing (NLP) based algorithms to analyze and understand the requirement corpus from a set of platform requirements. The training of the deep learning CNN algorithms is performed on a huge set of pre-implemented platform requirements. The inference of the new customer requirements is done using the trained deep learning-based models to classify the requirements into one of the pre-defined platform requirement classes, thereby assisting the manual analysis using its intelligent component by also providing the traceability.