What is the difference between naive Bayesian classifier and general Bayesian classifier?

Naive Bayesian classifier is naive because it assumes that each feature is independent.

Take the spam classifier as an example. If half of the spam in the training sample contains the words "promotional Viagra" and the other half contains the words "genuine Rolex", then the naive Bayesian classifier thinks that the probability of a spam containing both Viagra and Rolex is 0.25.

(Only consider the words "Viagra" and "Rolex")

As long as it is based on Bayesian theory, it can be called Bayesian classifier. Naive Bayesian classifier is called Naive Bayesian classifier because it is an optimization algorithm with high computational performance (the English page on Wikipedia only gives its definition).

Bayesian network is a statistical method based on Bayesian theory, which describes the global probability distribution in the form of DAG. It is not a classifier, but is mainly used for Bayesian reasoning.