There exist latent conflicts in multi-criteria group decision making (MCGDM): 1) alternatives may have opposite performance under different criteria, thus showing incomparability; 2) the opinions of different experts may differ greatly, leading to incomparability of alternatives and a group decision at poor consensus. Non-reciprocal fuzzy preference relation (NRFPR) can represent the incomparability; however, the assessment, aggregation and prioritization of NRFPRs need to be improved. Additionally, for the consensus issue, existing methods yielded a maximum consensus sequence (MCS) without experts’ compromise attitudes. To fill these research gaps, this study applies NRFPRs in MCGDM to handle the incomparability of alternatives, and mine the MCS based on experts’ compromise attitudes and information exchange. First, a distance-based method is applied to enhance the indirect assessment of NRFPRs. A layered preference aggregation method is developed to aggregate NRFPRs and a straightforward conflict analysis method is proposed to perform prioritization. Afterwards, a compromise-or-not mechanism is introduced to derive experts’ compromise attitudes. Then, two kinds of conflicting items requiring additional information are identified. Discussions among experts are activated to exchange information so as to mine an MCS. We apply our method to the site selection of electric vehicle charging stations.