Metrics show badly performing model for multiclass
I think you need to consider these metrics also i.e., Precision and Recall when you decide on some model.

As you can in the above outcome that there are Precision and Recall values, you din't explain anything about the problem's business perspective.

For example: Consider a data set with 100,000 observations. A data set consist of candidates who applied for Internship in Harvard. Apparently, harvard is well-known for its extremely low acceptance rate. The dependent variable represents if a candidate has been shortlisted (1) or not shortlisted (0). It was found ~ 98% did not get shortlisted and only ~ 2% got lucky. 
In this scenario you need to understand that precision and recall plays a vital role at the time of selecting model.

In the same way if your business problem is inline with the above scenario then you need to select a model with high Precision and Recall.

In the above screenshot which you have shared, it think you have good performance scores too(Assuming that your model is not over fitting). So, you can select which ever model suits your business problem.

Hope my answer is helpful!