The invention discloses a
human brain language
cognition modeling method. The
human brain language
cognition modeling method comprises the following steps of initialization of a cognitive state example, mapping of probability distribution between activation characteristics and
observation data, definition of a brain tacit
cognition model and
parameter analysis of the tacit cognition model. In the cognition modeling process, input stimulation, the observation result and the tacit cognition state are defined as triple time sequences related to a dynamic event, namely a cognition stimulation task
time sequence, an observation characteristic
time sequence and a tacit cognition state
time sequence, the triple time sequences are related to one another through a set of probability distribution, and not all collected brain data are treated as static information for statistics. Therefore, the
human brain language cognition modeling method does not need to meet the basic assumption based on statistics, is established under a
small sample data condition, and guarantees the
correctness of the cognition analysis result, thereby achieving cognition modeling under the
small sample data condition. The human brain language cognition modeling method improves the accuracy of cognition modeling, and provides an effective approach for complex cognition analysis.