Text representation is one of the most fundamental works in text comprehension, processing, and search. Various works have been proposed to mine the semantics in texts and then to represent them. However, most of them only focus on how to mine semantics from the text itself while the background knowledge, which is very important to text understanding, is not taken into consideration. In this paper, on the basis of human cognitive process, we propose a multi-level text representation model within background knowledge, called TRMBK. It is composed of three levels, which are machine surface code (MSC), machine text base (MTB) and machine situational model (MSM). All of the three are able to be automatically constructed to acquire semantics both inside and outside of the text. Simultaneously, we also propose a method to automatically establish background knowledge and offer supports for the current text comprehension. Finally, experiments and comparisons have been presented to show the better performance of TRMBK.