The invention relates to a 
coastal zone regional function long 
time sequence identification method based on multi-source 
big data, and the method comprises the following steps: firstly, constructing 
coastal zone regional function land classification based on human production, life and 
ecology, and carrying out the initial classification of a long 
time sequence image through employing a 
random forest algorithm based on a multi-source 
big data fusion platform; secondly, spatial separation of an 
impervious surface and a 
water body is achieved through a scanning line seed filling 
algorithm and geometric feature analysis, and functional types related to 
cultivated land transformation, offshore cultivation land, a 
salt pan and reclamation are corrected based on a temporal and 
spatial change logic rule; and finally, according to a 
classification result, change ranges and time stages of grain production, offshore culture and reclamation are extracted. According to the invention, the long-time-sequence identification precision of the regional functions of the 
coastal zone is improved, and the method is particularly suitable for regional function land transformation and 
change detection of reclamation construction caused by long-time-sequence, large-range and high-density offshore human activities, and can be directly applied to auxiliary 
decision making of 
spatial planning and regional policies of the coastal zone.