A learning strategy for fuzzy neural tree is presented that is based on combining the knowledge-driven and data-driven modeling paradigms. The knowledge-driven aspect of the strategy is expressing knowledge via the connection topology of a neural tree. The tree is driven by inputs associated with fuzzy logic. In this type of neural tree, the connection weights are determined in an unsupervised manner. However, the fuzzy logic related parameters are subject to data-driven identification, and they are comparatively few in number. For this reason, a low number of input-output data-pairs suffice to establish the neural representation in the new approach. This makes it suitable for representing evaluation processes of mind that have been difficult to bring into explicit form. An example of this is the evaluation of shape quality in an architectural design, and it is used to verify the effectiveness of the approach by experiment.