With the development of three-dimensional (3D) technologies, the demand for high-quality 3D content, 3D visualization, and flexible and natural interactions are increasing. As a result, semi-transparent Augmented-Reality (AR) systems are emerging and evolving rapidly. Since there are currently no well-recognized models to evaluate the performance of these systems, we proposed a Quality-of-Experience (QoE) taxonomy for semi-transparent AR systems containing three levels of influential QoE parameters, through analyzing existing QoE models in other related areas and integrating the feedbacks received from our user study. We designed a user study to collect training and testing data for our QoE model, and built a Fuzzy-Inference-System (FIS) model to estimate the QoE evaluation and validate the proposed taxonomy. A case study was also conducted to further explore the relationships between QoE parameters and technical QoS parameters with functional components of Microsoft HoloLens AR system. In this work, we illustrate the experiments in detail and thoroughly explain the results obtained. We also present the conclusion and future work.