Cognitive processing of individuals on food information affects food choices and eating behaviors, and high responsiveness to food cues is often a potential cause of overeating. The cognitive-motivational model proposes that for individuals with excess weight, high-calorie food was particularly rewarding, this type of food was an important cue grabbing their attention, and could increase the activation of brain areas that relate to reward processing, such food stimuli can more easily enter the memory system, which in turn produces memory bias. Although some studies have confirmed that overweight people have attention bias and memory bias towards food information, whether the attention bias of overweight groups on food information is a single attention component or a process of dynamic changes in multiple attention bias components has not yet reached a consensus conclusion. Besides, most studies on memory bias in food information processing among overweight do not consider the issue of food energy, so whether there is a difference in memory bias on high- and low-calorie foods by overweight people, their cognitive characteristics and processing methods need to be further proven. In total, 30 overweight female college students and 30 normal-weight female college students were tested successively with the dot-probe task and learning-recognition paradigm related to food pictures. A modified visual dot-probe paradigm was adopted to measure attention bias. We used 20 high-calorie food pictures, 20 low-calorie food pictures, and 60 neutral pictures as stimuli. A learning-recognition paradigm was adopted to measure memory bias. The participants have been presented with 96 old pictures and 96 new pictures (half each of high- and low-calorie food pictures) in a pseudo-random order, and then they were requested to judge whether or not the pictures had appeared in phase one. The results showed the following: (1) In the dot-probe task, the attention?vigilance index of overweight group on high-calorie food pictures was remarkably higher than that of the control group (F(1,58) = 7.718, p < .01, η2 = .117). However, there was no significant difference between overweight group and control group in the attention?vigilance index of low-calorie food pictures (F(1,58) = 1.179, p > .05). In the later stage of attention processing, the index of difficulty in attention disengagement of overweight group on high-calorie food pictures was tremendously higher than that in the control group (F(1,58) = 11.268, p = .001, η2 = .163). The index of difficulty in distracting attention from low-calorie food pictures in overweight group was notably lower than that of the control group, and it was presented as avoidance of attention (F(1,58) = 10.68, p < .01, η2 = .156). (2) The results of learning-recognition paradigm showed that the accuracy of recognition of high-calorie food pictures among overweight females was significantly higher than that of the control group (F(1,58) = 11.955, p = .001, η2 = .171), while the accuracy rate of recognition of low-calorie food pictures had no significant group difference (F(1,58) = .852, p > .05). This conclusion validated the cognitive motivation model, that was, overweight females were more sensitive and responsive to high-calorie food information with reward value and tended to invest more in acquiring motivation and cognitive resources. The results of this study help to uncover the inherent causes of overweight or weight gain, and provide practical solutions for designing food cognitive bias training programs for overweight groups as well.