The classical geostatistics methods, including kinds of Kringing and stochastic simulation methods, are the main approaches to research spatial distribution of geographical attribute. However, these methods have some shortcomings, including low quality and disable of making use of other valuable information effectively. In recent years, Bayesian Maximum Entropy is becoming widely used in various studies on evaluation of natural resources. This method is a new nonlinear method with more rigorous theoretical foundation than Kriging for integrating uncertain information into space mapping. It provides new and powerful means from incorporating various forms of physical knowledge (include hard and soft data) into space mapping process, and produces the complete probability distribution at each estimation point, thus allowing the calculation of elaborate statistics. This paper introduced a Bayesian Maximum Entropy approach with its data content, process, algorithm, result and sample of application. At last, advantages and disadvantages of the approach were analyzed.