Desert ants are particularly excellent navigators, able to use visual memories to learn long foraging routes and return to their nest in complex natural environments. The mushroom body (MB) is believed to be the key brain region that forms these visual memories. Specifically, the MBs can store two types of parallel and opposing valence memories, treated as attractive and repulsive, respectively. However, the underlying neural computations for integrating two opposing visual memories remain unclear. Based on biological findings, we propose two possible integration mechanisms for these opposing visual memories and implement both in our model. The first method, Subtraction-Based Integration, calculates the familiarity of the view in the current direction by computing the difference in the firing rates of two mushroom body output neurons (MBONs), which then guides directional decisions. The second method, Vector-Sum Integration, computes a preferred direction for each MBON, with the final movement direction determined by summing these directional vectors. We validated the effectiveness and testability of both integration methods by replicating recent biological experiments. Results demonstrates that both models could successfully replicate real ant’s trap-avoiding behavior, offering insights into the mechanisms by which insects could combine appetitive and aversive memories to make appropriate navigational decision.