Cognitive map is one of the hotspots in computing intelligence area. It is a qualitative inference technology, which provides a tool of soft computation to support self- adaptive behaviors that based on prior knowledge. However, cognitive map lacks of the capability of learning and self- adaptive. Image analysis and understanding are hotspots and difficult problems in image areas. Many methods have been proposed in research on image analysis and understanding, but few of them considered prior knowledge and computing intelligence simultaneously. Due to above problems, this dissertation discussed theory of cognitive map and its application in image analysis and understanding, whose mainly research is on learning of cognitive map, auto-knowledge acquiring of cognitive map and exploring cognitive map applications in image analysis and understanding, expecting to solve some drawbacks in the previous methods of image analysis and understanding.About the cognitive map theory: The stability of cognitive map is discussed firstly. For the study capability of cognitive map, objective datum is emphasized and the degree of unequilibrium is proposed to measure the difference between the cognitive map and the real world, so the expert knowledge and the objective datum can be unified in cognitive map.According to the values of the desire and theater concepts, a new idea is proposed which can acquire the knowledge of real world automatically.For the extend model of cognitive map, conditional probability, theory of uncertainty and knowledge database are introduced to cognitive map, and fuzzy cognitive map (FCM), probabilistic fuzzy cognitive map (PFCM), belief knowledge database based probabilistic fuzzy cognitive map (BKPFCM), "extended dynamic cognitive network" are presented. Therefore, those extended models can express the fuzzy and belief measure of uncertainty causal relationships and expert knowledge with uncertainty. Finally, the simulating capability of cognitive map is extended naturally.About the applications of cognitive map: Line feature detection and form based on fuzzy cognitive map, basic shapes recognition based on fuzzy cognitive map, object recognition based on probabilistic fuzzy cognitive map, a new method of image understanding based on cognitive map and robot high-level planning based on fuzzy cognitive map have been realized in this dissertation. Those methods can also go over some defects in previous methods of image analysis and understanding to a certain extent, which may be beneficial to developing some new methods in this field.