Based on the given feature "persons", which describes the capacity in terms of persons to carry in a car, the relationship between this feature and the task of rating the decision to buy the car can be analyzed.

Analyzing the feature "persons" in relation to the decision to buy the car, we can make the following assumptions:

1. Unacceptable: It is possible that cars with a capacity to carry only "2" persons might be considered unacceptable in terms of the decision to buy. Hence, "2" can be a value associated with the target class "unacceptable".

2. Acceptable: Cars with a capacity to carry "4" persons might be considered acceptable for purchasing. Hence, "4" can be a value associated with the target class "acceptable".

3. Good: Cars with a capacity to carry "more" persons might be considered good for purchasing, as it provides flexibility for accommodating more occupants. Therefore, "more" can be a value associated with the target class "good".

4. Very Good: Since the feature "persons" does not provide any specific information on the exact number of persons that the car can carry when it is "more", it is challenging to predict a specific value for the target class "very good". However, assuming that the capacity to carry more persons is generally seen as a positive aspect, we can include "more" as a value associated with the target class "very good".

Based on this analysis, the dictionary representing the relationship between the feature "persons" and the task of rating the decision to buy the car would be:

```json
{
	"unacceptable": ["2"],
	"acceptable": ["4"],
	"very good": ["more"],
	"good": ["more"]
}
```
Please note that the use of "more" for both "very good" and "good" is because the differentiation between "good" and "very good" based on the feature information is challenging. It is assumed that both "good" and "very good" would include cars with a capacity to carry more persons.