Abstract：Identifying Food-related Word Association and Topic Model Processing using LDA This paper presents an interdisciplinary study that combines natural language processing and psycholinguistics researchThe latent Dirichlet allocationLDAmodel was used for semantic relatedness computation to enable an understanding of the mechanisms and processes through which humans encode and retrieve lexical unitsTo test the similarity of the output of the topic model and human word associationtheTime-limited Multiple Divergent Thinking Test of Word Associative StrategyTLM-DTTWASwas used to collect data and conduct tests with three food-related stimulus wordsA total of 101 subjects took the testsproducing 4251 wordsThe empirical results were analyzed on two levels1by the expert word association classificationtaxonomic and script proposed by Ross and Murphy19992followed by the associative hierarchy theory of Mednick1962to sort the vocabulary test results into two associative hierarchiessteepandflatThe analysis indicated that human word association displays randomnessas well as generalization and continuityAfter the experimental text was passed through the LDA latent semantic model which demonstrated highly significant correlationThis was a whole new attempt to train a data science model to make inference and prediction of human concept association which could be very useful in teaching as well as commercial applicationsRetrieval and IndexingThis study combines the study of natural language treatment and psychological linguisticsUse the Latent Dirichlet AllocationLDAmodel to calculate the semantic correlation to understand how humans encode and search the mechanism and process of vocabulary unitsIn additionthe study also uses the LDA model to compare and correlation with human Ci association results