That is pretty funny considering your fit indices, CFAs assume multivariate normality, check that first as these violations can inflate χ2. Also don't know your sample size, χ is very sensitive to N, where for large N sometimes even miniscule discrepancies between the covariance matrices lead to high CMIN/df. But considering your other indices look so good I wouldn't worry too much about it, if there are some normality violations try again but with corrections like Bollen-stine or something similar

A chi-square value that is more than five times as large as its degrees of freedom points to substantial misfit/misspecification of your model. I don't think that non-normality alone accounts for this rather poor fit. Your model is quite large with many indicators and many factors. The poor chi-square fit indicates that there may be some inhomogeneities of the indicators (violations of unidimensionality). I would inspect standardized covariance residuals to examine which covariances are not well explained by your model.

The best solution is to examine the modification indices option in AMOS. That will point to how you can further improve your model.

There is a YouTube video on this: https://www.youtube.com/watch?v=LETR4B7qyEM

Furthermore, you need to check the standardized residual covariances (there is an option in AMOS) to check whether any of the items are above +1 or -1. In such cases, you may have to delete that item to secure a better model fit.

I would personally recommend using the modification indices option first. Please do try it and let me know what happens. All the best.