According to the basic principles and methods of information theory, the operation way of neural coding is studied and analyzed by using the minimum mutual information and the maximum entropy principle. This paper describes how the principles of minimum mutual information and maximum entropy are used to evaluate the amount of information in neural responses. Its main contribution is as follows: (1) that the expression of neural information is closely related to the utilization of neural energy, and it is found that the highly evolved nervous system strictly follows the two basic principles of economy and efficiency in energy consumption and utilization; (2) In order to verify the relationship between neural information processing and energy utilization, this paper uses the concept of energy-efficiency ratio to measure the economy and high efficiency of the nervous system in term of energy utilization by using the maximum entropy principle; (3) The numerical results show that the energy consumed by the nervous system reflects not only the internal law of neural information conduction and processing, but also the self-organization structure of neural information coding. The results suggest that energy neural coding, a novel neural information processing method, can be used to understand how brain activity works. Such a coding pattern can not only be extended to research the large-scale neuroscience field, but also unify brain models at all levels by use of the energy theory. This will provide a scientific theoretical basis for the exploration of how the brain works and the computational principles of brain-like artificial intelligence.