The calculation of air passenger flow density and the recognition of heat airspace in terminal areas of large hub airports is a new challenging research in the intelligent era, that is, using big data can automatically generate air passenger flow and basic rules. Aimed for the air passenger flow density in airport and its relationship between occupation and airspace, based on the establishment of the Beijing International Airport 1 h flight, which consists of basic parameters-latitude, longitude and height, additional parameters-dogleg and speed of trajectories, time parameters, a spatio-temporal data set by clustering trajectories and calculation of aircraft trajectories points was made up. Then, heat cloud map of flight trajectories under 4D conditions was generated. Cell was used to identify the basic parameters and time parameter of heat airspace; grid graphs of flight trajectories were used to identify additional parameters and explain the influence on heat airspace; probability fitting graphs were used to verify the accuracy of 4D results. The conclusion is as follows: there are two areas of Beijing International Airport, which have the high density and at two different heights there also exist hot peaks; flight trajectories and speed of trajectories also affect the heat airspace. The variation of heat airspace caused by different flight occupancy time in 4D recognition was revealed. The method realized the 4D heat cloud map of flight trajectories, which is better for detailed and accurate information construction, expression of spatial changes, and more the parameters of density and visualization of temporal and spatial range, which can assist the automatic dynamic airspace sectorization and decision making on airspace allocation, and also have a definite reference meaning in alleviating the contradiction between the current air traffic demand and limited airspace.