Feature importance of random forests
I can´t give you an perfect answer because there is no code, dataset and the target what you want to achieve.
Because the feature importances from random forest, is calculated based on the training data given to the model, not on predictions on a test dataset. That means, that is not the true prediction power. You should check, if there are difference on training and test results, when you run a random forest model. Another oportunity is the permutation feature importance.
But I made a similar experience and solved this on another way.

check correlation (shows linear dependency)
check with the pps (shows polynomial dependency)
https://datascienceplus.com/correlation-vs-pps-in-python/
check feature and permutation importance

With these 4 options, I got a better view into my dataset.
Hope I could help you a bit.